/train_data/cls/dataset
-```
-
-请参考下文组织您的数据。
-- 训练集
-
-首先建议将训练图片放入同一个文件夹，并用一个txt文件（cls_gt_train.txt）记录图片路径和标签。
-
-**注意：** 默认请将图片路径和图片标签用 `\t` 分割，如用其他方式分割将造成训练报错
-
-0和180分别表示图片的角度为0度和180度
-
-```
-" 图像文件名 图像标注信息 "
-train/cls/train/word_001.jpg 0
-train/cls/train/word_002.jpg 180
-```
-
-最终训练集应有如下文件结构：
-```
-|-train_data
- |-cls
- |- cls_gt_train.txt
- |- train
- |- word_001.png
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-- 测试集
-
-同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个cls_gt_test.txt，测试集的结构如下所示：
-
-```
-|-train_data
- |-cls
- |- cls_gt_test.txt
- |- test
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-## 3. 启动训练
-
-将准备好的txt文件和图片文件夹路径分别写入配置文件的 `Train/Eval.dataset.label_file_list` 和 `Train/Eval.dataset.data_dir` 字段下，`Train/Eval.dataset.data_dir`字段下的路径和文件里记载的图片名构成了图片的绝对路径。
-
-PaddleOCR提供了训练脚本、评估脚本和预测脚本。
-
-开始训练:
-
-*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
-
-```
-# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号。
-# 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
-python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
-```
-
-- 数据增强
-
-PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中取消`Train.dataset.transforms`下的`RecAug`和`RandAugment`字段的注释。
-
-默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
-
-训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
-[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
-[randaugment.py](../../ppocr/data/imaug/randaugment.py)
-
-*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
-
-
-## 4. 训练
-
-PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容：
-```bash
-├── best_accuracy.pdopt # 最佳模型的优化器参数
-├── best_accuracy.pdparams # 最佳模型的参数
-├── best_accuracy.states # 最佳模型的指标和epoch等信息
-├── config.yml # 本次实验的配置文件
-├── latest.pdopt # 最新模型的优化器参数
-├── latest.pdparams # 最新模型的参数
-├── latest.states # 最新模型的指标和epoch等信息
-└── train.log # 训练日志
-```
-
-如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
-
-**注意，预测/评估时的配置文件请务必与训练一致。**
-
-
-## 5. 评估
-
-评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。
-
-```
-export CUDA_VISIBLE_DEVICES=0
-# GPU 评估， Global.checkpoints 为待测权重
-python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
-```
-
-
-## 6. 预测
-
-* 训练引擎的预测
-
-使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
-
-通过 `Global.infer_img` 指定预测图片或文件夹路径，通过 `Global.checkpoints` 指定权重：
-
-```
-# 预测分类结果
-python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
-```
-
-预测图片：
-
-
-
-得到输入图像的预测结果：
-
-```
-infer_img: doc/imgs_words/ch/word_1.jpg
- result: ('0', 0.9998784)
-```
diff --git a/doc/doc_ch/application.md b/doc/doc_ch/application.md
deleted file mode 100644
index 9105a87854..0000000000
--- a/doc/doc_ch/application.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# 场景应用
-
-PaddleOCR场景应用覆盖通用，制造、金融、交通行业的主要OCR垂类应用，在PP-OCR、PP-Structure的通用能力基础之上，以notebook的形式展示利用场景数据微调、模型优化方法、数据增广等内容，为开发者快速落地OCR应用提供示范与启发。
-
-> 如需下载全部垂类模型，可以扫描下方二维码，关注公众号填写问卷后，加入PaddleOCR官方交流群获取20G OCR学习大礼包（内含《动手学OCR》电子书、课程回放视频、前沿论文等重磅资料）
-
-
-
-
-
-
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score |
-|:-:|:-:|:-:|:-:|:-:|:-:|
-| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 |
-| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 |
-| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 |
-
-
-预测模型在CPU和GPU上的速度对比，单位ms
-
-| 模型名称 | CPU | T4 GPU |
-|:-:|:-:|:-:|
-| PP-OCRv2 | 330 | 111 |
-| PP-OCR mobile | 356 | 11 6|
-| PP-OCR server | 1056 | 200 |
-
-更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchmark](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md)
diff --git a/doc/doc_ch/clone.md b/doc/doc_ch/clone.md
deleted file mode 100644
index 98a63d19a2..0000000000
--- a/doc/doc_ch/clone.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# 项目克隆
-
-## 1. 克隆PaddleOCR repo代码
-
-```
-【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
-```
-
-如果因为网络问题无法pull成功，也可选择使用码云上的托管：
-
-```
-git clone https://gitee.com/paddlepaddle/PaddleOCR
-```
-
-注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
-
-## 2. 安装第三方库
-
-```
-cd PaddleOCR
-pip3 install -r requirements.txt
-```
diff --git a/doc/doc_ch/code_and_doc.md b/doc/doc_ch/code_and_doc.md
deleted file mode 100644
index ce11015795..0000000000
--- a/doc/doc_ch/code_and_doc.md
+++ /dev/null
@@ -1,337 +0,0 @@
-# 附录
-
-本附录包含了Python、文档规范以及Pull Request流程，请各位开发者遵循相关内容
-
-- [附录1：Python代码规范](#附录1)
-
-- [附录2：文档规范](#附录2)
-
-- [附录3：Pull Request说明](#附录3)
-
-
-
-## 附录1：Python代码规范
-
-PaddleOCR的Python代码遵循 [PEP8规范](https://www.python.org/dev/peps/pep-0008/)，其中一些关注的重点包括如下内容
-
-- 空格
-
- - 空格应该加在逗号、分号、冒号后，而非他们的前面
-
- ```python
- # 正确：
- print(x, y)
-
- # 错误：
- print(x , y)
- ```
-
- - 在函数中指定关键字参数或默认参数值时, 不要在其两侧使用空格
-
- ```python
- # 正确：
- def complex(real, imag=0.0)
- # 错误：
- def complex(real, imag = 0.0)
- ```
-
-- 注释
-
- - 行内注释：行内注释使用 `#` 号表示，在代码与 `#` 之间需要空两个空格， `#` 与注释之间应当空一个空格，例如
-
- ```python
- x = x + 1 # Compensate for border
- ```
-
- - 函数和方法：每个函数的定义后的描述应该包括以下内容：
-
- - 函数描述：函数的作用，输入输出的
-
- - Args：每个参数的名字以及对该参数的描述
- - Returns：返回值的含义和类型
-
- ```python
- def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
- """Fetches rows from a Bigtable.
-
- Retrieves rows pertaining to the given keys from the Table instance
- represented by big_table. Silly things may happen if
- other_silly_variable is not None.
-
- Args:
- big_table: An open Bigtable Table instance.
- keys: A sequence of strings representing the key of each table row
- to fetch.
- other_silly_variable: Another optional variable, that has a much
- longer name than the other args, and which does nothing.
-
- Returns:
- A dict mapping keys to the corresponding table row data
- fetched. Each row is represented as a tuple of strings. For
- example:
-
- {'Serak': ('Rigel VII', 'Preparer'),
- 'Zim': ('Irk', 'Invader'),
- 'Lrrr': ('Omicron Persei 8', 'Emperor')}
-
- If a key from the keys argument is missing from the dictionary,
- then that row was not found in the table.
- """
- pass
- ```
-
-
-
-## 附录2：文档规范
-
-### 2.1 总体说明
-
-- 文档位置：如果您增加的新功能可以补充在原有的Markdown文件中，请**不要重新新建**一个文件。如果您对添加的位置不清楚，可以先PR代码，然后在commit中询问官方人员。
-
-- 新增Markdown文档名称：使用英文描述文档内容，一般由小写字母与下划线组合而成，例如 `add_new_algorithm.md`
-
-- 新增Markdown文档格式：目录 - 正文 - FAQ
-
- > 目录生成方法可以使用 [此网站](https://ecotrust-canada.github.io/markdown-toc/) 将md内容复制之后自动提取目录，然后在md文件的每个标题前添加 ``
-
-- 中英双语：任何对文档的改动或新增都需要分别在中文和英文文档上进行。
-
-### 2.2 格式规范
-
-- 标题格式：文档标题格式按照：阿拉伯数字小数点组合 - 空格 - 标题的格式（例如 `2.1 XXXX` ， `2. XXXX`）
-
-- 代码块：通过代码块格式展示需要运行的代码，在代码块前描述命令参数的含义。例如：
-
- > 检测+方向分类器+识别全流程：设置方向分类器参数 `--use_angle_cls true` 后可对竖排文本进行识别。
- >
- > ```
- > paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
- > ```
-
-- 变量引用：如果在行内引用到代码变量或命令参数，需要用行内代码表示，例如上方 `--use_angle_cls true` ，并在前后各空一格
-
-- 统一命名：如PP-OCRv2、PP-OCR mobile、`paddleocr` whl包、PPOCRLabel、Paddle Lite等
-
-- 补充说明：通过引用格式 `>` 补充说明，或对注意事项进行说明
-
-- 图片：如果在说明文档中增加了图片，请规范图片的命名形式（描述图片内容），并将图片添加在 `doc/` 下
-
-
-
-## 附录3：Pull Request说明
-
-### 3.1 PaddleOCR分支说明
-
-PaddleOCR未来将维护2种分支，分别为：
-
-- release/x.x系列分支：为稳定的发行版本分支，也是默认分支。PaddleOCR会根据功能更新情况发布新的release分支，同时适配Paddle的release版本。随着版本迭代，release/x.x系列分支会越来越多，默认维护最新版本的release分支。
-- dygraph分支：为开发分支，适配Paddle动态图的dygraph版本，主要用于开发新功能。如果有同学需要进行二次开发，请选择dygraph分支。为了保证dygraph分支能在需要的时候拉出release/x.x分支，dygraph分支的代码只能使用Paddle最新release分支中有效的api。也就是说，如果Paddle dygraph分支中开发了新的api，但尚未出现在release分支代码中，那么请不要在PaddleOCR中使用。除此之外，对于不涉及api的性能优化、参数调整、策略更新等，都可以正常进行开发。
-
-PaddleOCR的历史分支，未来将不再维护。考虑到一些同学可能仍在使用，这些分支还会继续保留：
-
-- develop分支：这个分支曾用于静态图的开发与测试，目前兼容>=1.7版本的Paddle。如果有特殊需求，要适配旧版本的Paddle，那还可以使用这个分支，但除了修复bug外不再更新代码。
-
-PaddleOCR欢迎大家向repo中积极贡献代码，下面给出一些贡献代码的基本流程。
-
-### 3.2 PaddleOCR代码提交流程与规范
-
-> 如果你熟悉Git使用，可以直接跳转到 [3.2.10 提交代码的一些约定](#提交代码的一些约定)
-
-#### 3.2.1 创建你的 `远程仓库`
-
-- 在PaddleOCR的 [GitHub首页](https://github.com/PaddlePaddle/PaddleOCR)，点击左上角 `Fork` 按钮，在你的个人目录下创建 `远程仓库`，比如`https://github.com/{your_name}/PaddleOCR`。
-
-
-
-- 将 `远程仓库` Clone到本地
-
-```
-# 拉取dygraph分支的代码
-git clone https://github.com/{your_name}/PaddleOCR.git -b dygraph
-cd PaddleOCR
-```
-
-> 多数情况下clone失败是由于网络原因，请稍后重试或配置代理
-
-#### 3.2.2 通过Token方式登录与建立连接
-
-首先查看当前 `远程仓库` 的信息。
-
-```
-git remote -v
-# origin https://github.com/{your_name}/PaddleOCR.git (fetch)
-# origin https://github.com/{your_name}/PaddleOCR.git (push)
-```
-
-只有clone的 `远程仓库` 的信息，也就是自己用户名下的 PaddleOCR。由于Github的登录方式变化，需要通过Token的方式重新配置 `远程仓库` 的地址。生成Token的方式如下：
-
-1. 找到个人访问令牌（token）：在Github页面右上角点击自己的头像，然后依次选择 Settings --> Developer settings --> Personal access tokens
-2. 点击 Generate new token：在Note中填入token名称，例如’paddle‘。在Select scopes选择repo（必选）、admin:repo_hook、delete_repo等，可根据自身需要勾选。然后点击Generate token生成token。最后复制生成的token。
-
-删除原始的origin配置
-
-```
-git remote rm origin
-```
-
-将remote分支改成 `https://oauth2:{token}@github.com/{your_name}/PaddleOCR.git`。例如：如果token值为12345，你的用户名为PPOCR，则运行下方命令
-
-```
-git remote add origin https://oauth2:12345@github.com/PPOCR/PaddleOCR.git
-```
-
-这样我们就与自己的 `远程仓库` 建立了连接。接下来我们创建一个原始 PaddleOCR 仓库的远程主机，命名为 upstream。
-
-```
-git remote add upstream https://github.com/PaddlePaddle/PaddleOCR.git
-```
-
-使用 `git remote -v` 查看当前 `远程仓库` 的信息，输出如下，发现包括了origin和upstream 2个 `远程仓库` 。
-
-```
-origin https://oauth2:{token}@github.com/{your_name}/PaddleOCR.git (fetch)
-origin https://oauth2:{token}@github.com/{your_name}/PaddleOCR.git (push)
-upstream https://github.com/PaddlePaddle/PaddleOCR.git (fetch)
-upstream https://github.com/PaddlePaddle/PaddleOCR.git (push)
-```
-
-这主要是为了后续在提交pull request(PR)时，始终保持本地仓库最新。
-
-#### 3.2.3 创建本地分支
-
-首先获取 upstream 的最新代码，然后基于上游仓库 (upstream)的dygraph创建new_branch分支。
-
-```
-git fetch upstream
-git checkout -b new_branch upstream/dygraph
-```
-
-> 如果对于新Fork的PaddleOCR项目，用户远程仓库(origin)与上游(upstream)仓库的分支更新情况相同，也可以基于origin仓库的默认分支或指定分支创建新的本地分支，命令如下。
->
-> ```
-> # 基于用户远程仓库(origin)的dygraph创建new_branch分支
-> git checkout -b new_branch origin/dygraph
->
-> # 基于用户远程仓库(origin)的默认分支创建new_branch分支
-> git checkout -b new_branch
-> ```
-
-最终会显示切换到新的分支，输出信息如下
-
-```
-Branch new_branch set up to track remote branch develop from upstream.
-Switched to a new branch 'new_branch'
-```
-
-切换分支之后即可在此分支上进行文件改动
-
-#### 3.2.4 使用pre-commit勾子
-
-Paddle 开发人员使用 pre-commit 工具来管理 Git 预提交钩子。 它可以帮助我们格式化源代码（C++，Python），在提交（commit）前自动检查一些基本事宜（如每个文件只有一个 EOL，Git 中不要添加大文件等）。
-
-pre-commit测试是 Travis-CI 中单元测试的一部分，不满足钩子的 PR 不能被提交到 PaddleOCR，首先安装并在当前目录运行它：
-
-```
-pip install pre-commit
-pre-commit install
-```
-
- > 1. Paddle 使用 clang-format 来调整 C/C++ 源代码格式，请确保 `clang-format` 版本在 3.8 以上。
- >
- > 2. 通过pip install pre-commit和conda install -c conda-forge pre-commit安装的yapf稍有不同的，PaddleOCR 开发人员使用的是 `pip install pre-commit`。
-
-#### 3.2.5 修改与提交代码
-
- 假设对PaddleOCR的 `README.md` 做了一些修改，可以通过 `git status` 查看改动的文件，然后使用 `git add` 添加改动文件。
-
-```
-git status # 查看改动文件
-git add README.md
-pre-commit
-```
-
-重复上述步骤，直到pre-comit格式检查不报错。如下所示。
-
-
-
-提交修改，并写明修改内容（"your commit info"）
-
-```
-git commit -m "your commit info"
-```
-
-#### 3.2.6 Push到远程仓库
-
-使用push命令将修改的commit提交到 `远程仓库`
-
-```
-git push origin new_branch
-```
-
-#### 3.2.7 提交Pull Request
-
-打开自己的远程仓库界面，选择提交的分支。点击new pull request或contribute进入PR界面。选择本地分支和目标分支，如下图所示。在PR的描述说明中，填写该PR所完成的功能。接下来等待review，如果有需要修改的地方，参照上述步骤更新 origin 中的对应分支即可。
-
-
-
-#### 3.2.8 签署CLA协议和通过单元测试
-
-- 签署CLA 在首次向PaddlePaddle提交Pull Request时，您需要您签署一次CLA(Contributor License Agreement)协议，以保证您的代码可以被合入，具体签署方式如下：
-
- 1. 请您查看PR中的Check部分，找到license/cla，并点击右侧detail，进入CLA网站
-
- 2. 点击CLA网站中的“Sign in with GitHub to agree”,点击完成后将会跳转回您的Pull Request页面
-
-#### 3.2.9 删除分支
-
-- 删除远程分支
-
- 在 PR 被 merge 进主仓库后，我们可以在 PR 的页面删除远程仓库的分支。
-
- 也可以使用 `git push origin :分支名` 删除远程分支，如：
-
- ```
- git push origin :new_branch
- ```
-
-- 删除本地分支
-
- ```
- # 切换到dygraph分支，否则无法删除当前分支
- git checkout dygraph
-
- # 删除new_branch分支
- git branch -D new_branch
- ```
-
-
-
-#### 3.2.10 提交代码的一些约定
-
-为了使官方维护人员在评审代码时更好地专注于代码本身，请您每次提交代码时，遵守以下约定：
-
-1）请保证Travis-CI 中单元测试能顺利通过。如果没过，说明提交的代码存在问题，官方维护人员一般不做评审。
-
-2）提交Pull Request前：
-
-- 请注意commit的数量。
-
- 原因：如果仅仅修改一个文件但提交了十几个commit，每个commit只做了少量的修改，这会给评审人带来很大困扰。评审人需要逐一查看每个commit才能知道做了哪些修改，且不排除commit之间的修改存在相互覆盖的情况。
-
- 建议：每次提交时，保持尽量少的commit，可以通过git commit --amend补充上次的commit。对已经Push到远程仓库的多个commit，可以参考[squash commits after push](https://stackoverflow.com/questions/5667884/how-to-squash-commits-in-git-after-they-have-been-pushed)。
-
-- 请注意每个commit的名称：应能反映当前commit的内容，不能太随意。
-
-3）如果解决了某个Issue的问题，请在该Pull Request的第一个评论框中加上：fix #issue_number，这样当该Pull Request被合并后，会自动关闭对应的Issue。关键词包括：close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved，请选择合适的词汇。详细可参考[Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages)。
-
-此外，在回复评审人意见时，请您遵守以下约定：
-
-1）官方维护人员的每一个review意见都希望得到回复，这样会更好地提升开源社区的贡献。
-
-- 对评审意见同意且按其修改完的，给个简单的Done即可；
-- 对评审意见不同意的，请给出您自己的反驳理由。
-
-2）如果评审意见比较多:
-
-- 请给出总体的修改情况。
-- 请采用`start a review`进行回复，而非直接回复的方式。原因是每个回复都会发送一封邮件，会造成邮件灾难。
diff --git a/doc/doc_ch/config.md b/doc/doc_ch/config.md
deleted file mode 100644
index 5ca1f68e6f..0000000000
--- a/doc/doc_ch/config.md
+++ /dev/null
@@ -1,226 +0,0 @@
-# 配置文件内容与生成
-
-* [1. 可选参数列表](#1)
-* [2. 配置文件参数介绍](#2)
-* [3. 多语言配置文件生成](#3)
-
-
-
-## 1. 可选参数列表
-
-以下列表可以通过`--help`查看
-
-| FLAG | 支持脚本 | 用途 | 默认值 | 备注 |
-| :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: |
-| -c | ALL | 指定配置文件 | None | **配置模块说明请参考 参数介绍** |
-| -o | ALL | 设置配置文件里的参数内容 | None | 使用-o配置相较于-c选择的配置文件具有更高的优先级。例如：`-o Global.use_gpu=false` |
-
-
-
-## 2. 配置文件参数介绍
-
-以 `rec_chinese_lite_train_v2.0.yml ` 为例
-### Global
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :----------------------: | :---------------------: | :--------------: | :--------------------: |
-| use_gpu | 设置代码是否在gpu运行 | true | \ |
-| epoch_num | 最大训练epoch数 | 500 | \ |
-| log_smooth_window | log队列长度，每次打印输出队列里的中间值 | 20 | \ |
-| print_batch_step | 设置打印log间隔 | 10 | \ |
-| save_model_dir | 设置模型保存路径 | output/{算法名称} | \ |
-| save_epoch_step | 设置模型保存间隔 | 3 | \ |
-| eval_batch_step | 设置模型评估间隔 | 2000 或 [1000, 2000] | 2000 表示每2000次迭代评估一次，[1000， 2000]表示从1000次迭代开始，每2000次评估一次 |
-| cal_metric_during_train | 设置是否在训练过程中评估指标，此时评估的是模型在当前batch下的指标 | true | \ |
-| load_static_weights | 设置预训练模型是否是静态图模式保存(目前仅检测算法需要) | true | \ |
-| pretrained_model | 设置加载预训练模型路径 | ./pretrain_models/CRNN/best_accuracy | \ |
-| checkpoints | 加载模型参数路径 | None | 用于中断后加载参数继续训练 |
-| use_visualdl | 设置是否启用visualdl进行可视化log展示 | False | [教程地址](https://www.paddlepaddle.org.cn/paddle/visualdl) |
-| infer_img | 设置预测图像路径或文件夹路径 | ./infer_img | \||
-| character_dict_path | 设置字典路径 | ./ppocr/utils/ppocr_keys_v1.txt | 如果为空，则默认使用小写字母+数字作为字典 |
-| max_text_length | 设置文本最大长度 | 25 | \ |
-| use_space_char | 设置是否识别空格 | True | \| |
-| label_list | 设置方向分类器支持的角度 | ['0','180'] | 仅在方向分类器中生效 |
-| save_res_path | 设置检测模型的结果保存地址 | ./output/det_db/predicts_db.txt | 仅在检测模型中生效 |
-
-### Optimizer ([ppocr/optimizer](../../ppocr/optimizer))
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: |:-------------:|:-------------:| :--------------------: |
-| name | 优化器类名 | Adam | 目前支持`Momentum`,`Adam`,`RMSProp`, 见[ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) |
-| beta1 | 设置一阶矩估计的指数衰减率 | 0.9 | \ |
-| beta2 | 设置二阶矩估计的指数衰减率 | 0.999 | \ |
-| clip_norm | 所允许的二范数最大值 | | \ |
-| **lr** | 设置学习率decay方式 | - | \ |
-| name | 学习率decay类名 | Cosine | 目前支持`Linear`,`Cosine`,`Step`,`Piecewise`, 见[ppocr/optimizer/learning_rate.py](../../ppocr/optimizer/learning_rate.py) |
-| learning_rate | 基础学习率 | 0.001 | \ |
-| **regularizer** | 设置网络正则化方式 | - | \ |
-| name | 正则化类名 | L2 | 目前支持`L1`,`L2`, 见[ppocr/optimizer/regularizer.py](../../ppocr/optimizer/regularizer.py) |
-| factor | 正则化系数 | 0.00001 | \ |
-
-
-### Architecture ([ppocr/modeling](../../ppocr/modeling))
-在PaddleOCR中，网络被划分为Transform,Backbone,Neck和Head四个阶段
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| model_type | 网络类型 | rec | 目前支持`rec`,`det`,`cls` |
-| algorithm | 模型名称 | CRNN | 支持列表见[algorithm_overview](./algorithm_overview.md) |
-| **Transform** | 设置变换方式 | - | 目前仅rec类型的算法支持, 具体见[ppocr/modeling/transforms](../../ppocr/modeling/transforms) |
-| name | 变换方式类名 | TPS | 目前支持`TPS` |
-| num_fiducial | TPS控制点数 | 20 | 上下边各十个 |
-| loc_lr | 定位网络学习率 | 0.1 | \ |
-| model_name | 定位网络大小 | small | 目前支持`small`,`large` |
-| **Backbone** | 设置网络backbone类名 | - | 具体见[ppocr/modeling/backbones](../../ppocr/modeling/backbones) |
-| name | backbone类名 | ResNet | 目前支持`MobileNetV3`,`ResNet` |
-| layers | resnet层数 | 34 | 支持18,34,50,101,152,200 |
-| model_name | MobileNetV3 网络大小 | small | 支持`small`,`large` |
-| **Neck** | 设置网络neck | - | 具体见[ppocr/modeling/necks](../../ppocr/modeling/necks) |
-| name | neck类名 | SequenceEncoder | 目前支持`SequenceEncoder`,`DBFPN` |
-| encoder_type | SequenceEncoder编码器类型 | rnn | 支持`reshape`,`fc`,`rnn` |
-| hidden_size | rnn内部单元数 | 48 | \ |
-| out_channels | DBFPN输出通道数 | 256 | \ |
-| **Head** | 设置网络Head | - | 具体见[ppocr/modeling/heads](../../ppocr/modeling/heads) |
-| name | head类名 | CTCHead | 目前支持`CTCHead`,`DBHead`,`ClsHead` |
-| fc_decay | CTCHead正则化系数 | 0.0004 | \ |
-| k | DBHead二值化系数 | 50 | \ |
-| class_dim | ClsHead输出分类数 | 2 | \ |
-
-
-### Loss ([ppocr/losses](../../ppocr/losses))
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | 网络loss类名 | CTCLoss | 目前支持`CTCLoss`,`DBLoss`,`ClsLoss` |
-| balance_loss | DBLossloss中是否对正负样本数量进行均衡(使用OHEM) | True | \ |
-| ohem_ratio | DBLossloss中的OHEM的负正样本比例 | 3 | \ |
-| main_loss_type | DBLossloss中shrink_map所采用的loss | DiceLoss | 支持`DiceLoss`,`BCELoss` |
-| alpha | DBLossloss中shrink_map_loss的系数 | 5 | \ |
-| beta | DBLossloss中threshold_map_loss的系数 | 10 | \ |
-
-### PostProcess ([ppocr/postprocess](../../ppocr/postprocess))
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | 后处理类名 | CTCLabelDecode | 目前支持`CTCLoss`,`AttnLabelDecode`,`DBPostProcess`,`ClsPostProcess` |
-| thresh | DBPostProcess中分割图进行二值化的阈值 | 0.3 | \ |
-| box_thresh | DBPostProcess中对输出框进行过滤的阈值，低于此阈值的框不会输出 | 0.7 | \ |
-| max_candidates | DBPostProcess中输出的最大文本框数量 | 1000 | |
-| unclip_ratio | DBPostProcess中对文本框进行放大的比例 | 2.0 | \ |
-
-### Metric ([ppocr/metrics](../../ppocr/metrics))
-
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | 指标评估方法名称 | CTCLabelDecode | 目前支持`DetMetric`,`RecMetric`,`ClsMetric` |
-| main_indicator | 主要指标,用于选取最优模型 | acc | 对于检测方法为hmean，识别和分类方法为acc |
-
-### Dataset ([ppocr/data](../../ppocr/data))
-| 字段 | 用途 | 默认值 | 备注 |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| **dataset** | 每次迭代返回一个样本 | - | - |
-| name | dataset类名 | SimpleDataSet | 目前支持`SimpleDataSet`和`LMDBDataSet` |
-| data_dir | 数据集图片存放路径 | ./train_data | \ |
-| label_file_list | 数据标签路径 | ["./train_data/train_list.txt"] | dataset为LMDBDataSet时不需要此参数 |
-| ratio_list | 数据集的比例 | [1.0] | 若label_file_list中有两个train_list，且ratio_list为[0.4,0.6]，则从train_list1中采样40%，从train_list2中采样60%组合整个dataset |
-| transforms | 对图片和标签进行变换的方法列表 | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | 见[ppocr/data/imaug](../../ppocr/data/imaug) |
-| **loader** | dataloader相关 | - | |
-| shuffle | 每个epoch是否将数据集顺序打乱 | True | \ |
-| batch_size_per_card | 训练时单卡batch size | 256 | \ |
-| drop_last | 是否丢弃因数据集样本数不能被 batch_size 整除而产生的最后一个不完整的mini-batch | True | \ |
-| num_workers | 用于加载数据的子进程个数，若为0即为不开启子进程，在主进程中进行数据加载 | 8 | \ |
-
-
-
-## 3. 多语言配置文件生成
-
-PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
-
-您有两种方式创建所需的配置文件：
-
-1. 通过脚本自动生成
-
-[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) 可以帮助您生成多语言模型的配置文件
-
-- 以意大利语为例，如果您的数据是按如下格式准备的：
- ```
- |-train_data
- |- it_train.txt # 训练集标签
- |- it_val.txt # 验证集标签
- |- data
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
- ```
-
- 可以使用默认参数，生成配置文件：
-
- ```bash
- # 该代码需要在指定目录运行
- cd PaddleOCR/configs/rec/multi_language/
- # 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件
- python3 generate_multi_language_configs.py -l it
- ```
-
-- 如果您的数据放置在其他位置，或希望使用自己的字典，可以通过指定相关参数来生成配置文件:
-
- ```bash
- # -l或者--language字段是必须的
- # --train修改训练集，--val修改验证集，--data_dir修改数据集目录，--dict修改字典路径， -o修改对应默认参数
- cd PaddleOCR/configs/rec/multi_language/
- python3 generate_multi_language_configs.py -l it \ # 语种
- --train {path/of/train_label.txt} \ # 训练标签文件的路径
- --val {path/of/val_label.txt} \ # 验证集标签文件的路径
- --data_dir {train_data/path} \ # 训练数据的根目录
- --dict {path/of/dict} \ # 字典文件路径
- -o Global.use_gpu=False # 是否使用gpu
- ...
-
- ```
-
-意大利文由拉丁字母组成，因此执行完命令后会得到名为 rec_latin_lite_train.yml 的配置文件。
-
-2. 手动修改配置文件
-
- 您也可以手动修改模版中的以下几个字段得到配置文件:
-
- ```
- Global:
- use_gpu: True
- epoch_num: 500
- ...
- character_dict_path: {path/of/dict} # 字典文件所在路径
-
- Train:
- dataset:
- name: SimpleDataSet
- data_dir: train_data/ # 数据存放根目录
- label_file_list: ["./train_data/train_list.txt"] # 训练集label路径
- ...
-
- Eval:
- dataset:
- name: SimpleDataSet
- data_dir: train_data/ # 数据存放根目录
- label_file_list: ["./train_data/val_list.txt"] # 验证集label路径
- ...
-
- ```
-
-目前PaddleOCR支持的多语言算法有：
-
-| 配置文件 | 算法名称 | backbone | trans | seq | pred | language |
-| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
-| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 中文繁体 |
-| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语（区分大小写） |
-| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 |
-| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 |
-| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 |
-| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 |
-| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 拉丁字母 |
-| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯字母 |
-| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 斯拉夫字母 |
-| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 梵文字母 |
-
-更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md)
diff --git a/doc/doc_ch/customize.md b/doc/doc_ch/customize.md
deleted file mode 100644
index 3da61ab44b..0000000000
--- a/doc/doc_ch/customize.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# 如何生产自定义超轻量模型？
-
-生产自定义的超轻量模型可分为三步：训练文本检测模型、训练文本识别模型、模型串联预测。
-
-## step1：训练文本检测模型
-
-PaddleOCR提供了EAST、DB两种文本检测算法，均支持MobileNetV3、ResNet50_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的DB检测模型（即超轻量模型使用的配置）：
-```
-python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
-```
-更详细的数据准备和训练教程参考文档教程中[文本检测模型训练/评估/预测](./detection.md)。
-
-## step2：训练文本识别模型
-
-PaddleOCR提供了CRNN、Rosetta、STAR-Net、RARE四种文本识别算法，均支持MobileNetV3、ResNet34_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的CRNN识别模型（即超轻量模型使用的配置）：
-```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
-```
-更详细的数据准备和训练教程参考文档教程中[文本识别模型训练/评估/预测](./recognition.md)。
-
-## step3：模型串联预测
-
-PaddleOCR提供了检测和识别模型的串联工具，可以将训练好的任一检测模型和任一识别模型串联成两阶段的文本识别系统。输入图像经过文本检测、检测框矫正、文本识别、得分过滤四个主要阶段输出文本位置和识别结果，同时可选择对结果进行可视化。
-
-在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
-
-```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
-```
-更多的文本检测、识别串联推理使用方式请参考文档教程中的[基于预测引擎推理](./algorithm_inference.md)。
diff --git a/doc/doc_ch/data_annotation.md b/doc/doc_ch/data_annotation.md
deleted file mode 100644
index bd578f7283..0000000000
--- a/doc/doc_ch/data_annotation.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## 数据标注工具
-这里整理了常用的数据标注工具，持续更新中，欢迎各位小伙伴贡献工具～
-
-
-### 1. labelImg
-- 工具描述：矩形标注
-- 工具地址：https://github.com/tzutalin/labelImg
-- 示意图：
- 
-
-### 2. roLabelImg
-- 工具描述：基于labelImg重写的标注工具，支持旋转矩形标注
-- 工具地址：https://github.com/cgvict/roLabelImg
-- 示意图：
- 
-
-### 3. labelme
-- 工具描述：支持四点、多边形、圆形等多种标注
-- 工具地址：https://github.com/wkentaro/labelme
-- 示意图：
- 
-
-### 4. Vott
-- 工具描述：支持矩形,多边形等图片标注.支持视频标注.方便使用的快捷键以及比较好看的界面.同时支持导出多种标签格式.
-- 工具地址：https://github.com/microsoft/VoTT
-- 示意图：
- 
diff --git a/doc/doc_ch/data_synthesis.md b/doc/doc_ch/data_synthesis.md
deleted file mode 100644
index 0b2b95af16..0000000000
--- a/doc/doc_ch/data_synthesis.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# 数据合成工具
-除了开源数据，用户还可使用合成工具自行合成。这里整理了常用的数据合成工具，持续更新中，欢迎各位小伙伴贡献工具～
-- [text_renderer](https://github.com/Sanster/text_renderer)
-- [SynthText](https://github.com/ankush-me/SynthText)
-- [SynthText_Chinese_version](https://github.com/JarveeLee/SynthText_Chinese_version)
-- [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator)
-- [SynthText3D](https://github.com/MhLiao/SynthText3D)
-- [UnrealText](https://github.com/Jyouhou/UnrealText/)
-- [SynthTIGER](https://github.com/clovaai/synthtiger)
diff --git a/doc/doc_ch/dataset/datasets.md b/doc/doc_ch/dataset/datasets.md
deleted file mode 100644
index 84fd9650b3..0000000000
--- a/doc/doc_ch/dataset/datasets.md
+++ /dev/null
@@ -1,88 +0,0 @@
-## 通用中英文OCR数据集
-这里整理了常用中文数据集，持续更新中，欢迎各位小伙伴贡献数据集～
-- [ICDAR2019-LSVT](#ICDAR2019-LSVT)
-- [ICDAR2017-RCTW-17](#ICDAR2017-RCTW-17)
-- [中文街景文字识别](#中文街景文字识别)
-- [中文文档文字识别](#中文文档文字识别)
-- [ICDAR2019-ArT](#ICDAR2019-ArT)
-- [电子印章数据集](#电子印章数据集)
-
-除了开源数据，用户还可使用合成工具自行合成，可参考[数据合成工具](../data_synthesis.md)；
-
-如果需要标注自己的数据，可参考[数据标注工具](../data_annotation.md)。
-
-
-#### 1、ICDAR2019-LSVT
-- **数据来源**：https://ai.baidu.com/broad/introduction?dataset=lsvt
-- **数据简介**： 共45w中文街景图像，包含5w（2w测试+3w训练）全标注数据（文本坐标+文本内容），40w弱标注数据（仅文本内容），如下图所示：
- 
- (a) 全标注数据
- 
- (b) 弱标注数据
-- **下载地址**：https://ai.baidu.com/broad/download?dataset=lsvt
-- **说明**：其中，test数据集的label目前没有开源，如要评估结果，可以去官网提交：https://rrc.cvc.uab.es/?ch=16
-
-
-#### 2、ICDAR2017-RCTW-17
-- **数据来源**：https://rctw.vlrlab.net/
-- **数据简介**：共包含12,000+图像，大部分图片是通过手机摄像头在野外采集的。有些是截图。这些图片展示了各种各样的场景，包括街景、海报、菜单、室内场景和手机应用程序的截图。
- 
-- **下载地址**：https://rctw.vlrlab.net/dataset/
-
-
-#### 3、中文街景文字识别
-- **数据来源**：https://aistudio.baidu.com/aistudio/competition/detail/8
-- **数据简介**：ICDAR2019-LSVT行识别任务，共包括29万张图片，其中21万张图片作为训练集（带标注），8万张作为测试集（无标注）。数据集采自中国街景，并由街景图片中的文字行区域（例如店铺标牌、地标等等）截取出来而形成。所有图像都经过一些预处理，将文字区域利用仿射变化，等比映射为一张高为48像素的图片，如图所示：
- 
- (a) 标注：魅派集成吊顶
- 
- (b) 标注：母婴用品连锁
-- **下载地址**
-https://aistudio.baidu.com/aistudio/datasetdetail/8429
-
-
-#### 4、中文文档文字识别
-- **数据来源**：https://github.com/YCG09/chinese_ocr
-- **数据简介**：
- - 共约364万张图片，按照99:1划分成训练集和验证集。
- - 数据利用中文语料库（新闻 + 文言文），通过字体、大小、灰度、模糊、透视、拉伸等变化随机生成
- - 包含汉字、英文字母、数字和标点共5990个字符（字符集合：https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt ）
- - 每个样本固定10个字符，字符随机截取自语料库中的句子
- - 图片分辨率统一为280x32
- 
- 
-- **下载地址**：https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (密码：lu7m)
-
-
-#### 5、ICDAR2019-ArT
-- **数据来源**：https://ai.baidu.com/broad/introduction?dataset=art
-- **数据简介**：共包含10,166张图像，训练集5603图，测试集4563图。由Total-Text、SCUT-CTW1500、Baidu Curved Scene Text (ICDAR2019-LSVT部分弯曲数据) 三部分组成，包含水平、多方向和弯曲等多种形状的文本。
- 
-- **下载地址**：https://ai.baidu.com/broad/download?dataset=art
-
-
-#### 6、电子印章数据集
-- **数据来源**：https://aistudio.baidu.com/aistudio/datasetdetail/154271/0
-- **数据简介**：共包含10000张图像，训练集8000图，测试集2000图。数据集是用程序合成的，并不涉及隐私安全，主要用于印章弯曲文本的训练与检测。由开发者[jingsongliujing](https://github.com/jingsongliujing)贡献
-- **下载地址**：https://aistudio.baidu.com/aistudio/datasetdetail/154271/0
-
-## 参考文献
-**ICDAR 2019-LSVT Challenge**
-```
-@article{sun2019icdar,
- title={ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling--RRC-LSVT},
- author={Sun, Yipeng and Ni, Zihan and Chng, Chee-Kheng and Liu, Yuliang and Luo, Canjie and Ng, Chun Chet and Han, Junyu and Ding, Errui and Liu, Jingtuo and Karatzas, Dimosthenis and others},
- journal={arXiv preprint arXiv:1909.07741},
- year={2019}
-}
-```
-
-**ICDAR 2019-ArT Challenge**
-```
-@article{chng2019icdar2019,
- title={ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)},
- author={Chng, Chee-Kheng and Liu, Yuliang and Sun, Yipeng and Ng, Chun Chet and Luo, Canjie and Ni, Zihan and Fang, ChuanMing and Zhang, Shuaitao and Han, Junyu and Ding, Errui and others},
- journal={arXiv preprint arXiv:1909.07145},
- year={2019}
-}
-```
diff --git a/doc/doc_ch/dataset/handwritten_datasets.md b/doc/doc_ch/dataset/handwritten_datasets.md
deleted file mode 100644
index 6485870cdf..0000000000
--- a/doc/doc_ch/dataset/handwritten_datasets.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# 手写OCR数据集
-这里整理了常用手写数据集，持续更新中，欢迎各位小伙伴贡献数据集～
-- [中科院自动化研究所-手写中文数据集](#中科院自动化研究所-手写中文数据集)
-- [NIST手写单字数据集-英文](#NIST手写单字数据集-英文)
-
-
-## 中科院自动化研究所-手写中文数据集
-- **数据来源**：http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
-- **数据简介**：
- * 包含在线和离线两类手写数据，`HWDB1.0~1.2`总共有3895135个手写单字样本，分属7356类（7185个汉字和171个英文字母、数字、符号）；`HWDB2.0~2.2`总共有5091页图像，分割为52230个文本行和1349414个文字。所有文字和文本样本均存为灰度图像。部分单字样本图片如下所示。
-
- 
-
-- **下载地址**：http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
-- **使用建议**：数据为单字，白色背景，可以大量合成文字行进行训练。白色背景可以处理成透明状态，方便添加各种背景。对于需要语义的情况，建议从真实语料出发，抽取单字组成文字行
-
-
-
-## NIST手写单字数据集-英文(NIST Handprinted Forms and Characters Database)
-
-- **数据来源**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
-
-- **数据简介**: NIST19数据集适用于手写文档和字符识别的模型训练，从3600位作者的手写样本表格中提取得到，总共包含81万张字符图片。其中9张图片示例如下。
-
- 
-
-
-- **下载地址**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
diff --git a/doc/doc_ch/dataset/kie_datasets.md b/doc/doc_ch/dataset/kie_datasets.md
deleted file mode 100644
index be5624dbf2..0000000000
--- a/doc/doc_ch/dataset/kie_datasets.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# 关键信息抽取数据集
-
-这里整理了常见的关键信息抽取数据集，持续更新中，欢迎各位小伙伴贡献数据集～
-
-- [FUNSD数据集](#funsd)
-- [XFUND数据集](#xfund)
-- [wildreceipt数据集](#wildreceipt)
-
-
-
-## 1. FUNSD数据集
-
-- **数据来源**：https://guillaumejaume.github.io/FUNSD/
-- **数据简介**：FUNSD数据集是一个用于表单理解的数据集，它包含199张真实的、完全标注的扫描版图片，类型包括市场报告、广告以及学术报告等，并分为149张训练集以及50张测试集。FUNSD数据集适用于多种类型的DocVQA任务，如字段级实体分类、字段级实体连接等。部分图像以及标注框可视化如下所示:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- 
-
-
-将下载到的数据集解压到工作目录下，假设解压在 PaddleOCR/train_data/下。然后从上表中下载转换好的标注文件。
-
-PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
-
-```
-# 将官网下载的标签文件转换为 train_icdar2015_label.txt
-python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/" \
- --input_path="/path/to/ch4_training_localization_transcription_gt" \
- --output_label="/path/to/train_icdar2015_label.txt"
-```
-
-解压数据集和下载标注文件后，PaddleOCR/train_data/ 有两个文件夹和两个文件，按照如下方式组织icdar2015数据集：
-```
-/PaddleOCR/train_data/icdar2015/text_localization/
- └─ icdar_c4_train_imgs/ icdar 2015 数据集的训练数据
- └─ ch4_test_images/ icdar 2015 数据集的测试数据
- └─ train_icdar2015_label.txt icdar 2015 数据集的训练标注
- └─ test_icdar2015_label.txt icdar 2015 数据集的测试标注
-```
-
-## 2. 文本识别
-
-### 2.1 PaddleOCR 文字识别数据格式
-
-PaddleOCR 中的文字识别算法支持两种数据格式:
-
- - `lmdb` 用于训练以lmdb格式存储的数据集，使用 [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) 进行读取;
- - `通用数据` 用于训练以文本文件存储的数据集，使用 [simple_dataset.py](../../../ppocr/data/simple_dataset.py)进行读取。
-
-下面以通用数据集为例， 介绍如何准备数据集：
-
-* 训练集
-
-建议将训练图片放入同一个文件夹，并用一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下:
-
-**注意：** txt文件中默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错。
-
-```
-" 图像文件名 图像标注信息 "
-
-train_data/rec/train/word_001.jpg 简单可依赖
-train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
-...
-```
-
-最终训练集应有如下文件结构：
-```
-|-train_data
- |-rec
- |- rec_gt_train.txt
- |- train
- |- word_001.png
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-除上述单张图像为一行格式之外，PaddleOCR也支持对离线增广后的数据进行训练，为了防止相同样本在同一个batch中被多次采样，我们可以将相同标签对应的图片路径写在一行中，以列表的形式给出，在训练中，PaddleOCR会随机选择列表中的一张图片进行训练。对应地，标注文件的格式如下。
-
-```
-["11.jpg", "12.jpg"] 简单可依赖
-["21.jpg", "22.jpg", "23.jpg"] 用科技让复杂的世界更简单
-3.jpg ocr
-```
-
-上述示例标注文件中，"11.jpg"和"12.jpg"的标签相同，都是`简单可依赖`，在训练的时候，对于该行标注，会随机选择其中的一张图片进行训练。
-
-
-- 验证集
-
-同训练集类似，验证集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，验证集的结构如下所示：
-
-```
-|-train_data
- |-rec
- |- rec_gt_test.txt
- |- test
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-
-### 2.2 公开数据集
-
-| 数据集名称 | 图片下载地址 | PaddleOCR 标注下载地址 |
-|---|---|---------------------------------------------------------------------|
-| en benchmark(MJ, SJ, IIIT, SVT, IC03, IC13, IC15, SVTP, and CUTE.) | [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | LMDB格式，可直接用[lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py)加载 |
-|ICDAR 2015| http://rrc.cvc.uab.es/?ch=4&com=downloads | [train](https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt)/ [test](https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt) |
-| 多语言数据集 |[百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 提取码：frgi
[google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) | 图片下载地址中已包含 |
-
-#### 2.1 ICDAR 2015
-
-ICDAR 2015 数据集可以在上表中链接下载，用于快速验证。也可以从上表中下载 en benchmark 所需的lmdb格式数据集。
-
-下载完图片后从上表中下载转换好的标注文件。
-
-PaddleOCR 也提供了数据格式转换脚本，可以将ICDAR官网 label 转换为PaddleOCR支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
-
-```
-# 将官网下载的标签文件转换为 rec_gt_label.txt
-python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
-```
-
-数据样式格式如下，(a)为原始图片,(b)为每张图片对应的 Ground Truth 文本文件：
-
-
-
-## 3. 数据存放路径
-
-PaddleOCR训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
-
-```
-# linux and mac os
-ln -sf /train_data/dataset
-# windows
-mklink /d /train_data/dataset
-```
diff --git a/doc/doc_ch/dataset/table_datasets.md b/doc/doc_ch/dataset/table_datasets.md
deleted file mode 100644
index 58f4cf4705..0000000000
--- a/doc/doc_ch/dataset/table_datasets.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# 表格识别数据集
-
-- [数据集汇总](#数据集汇总)
-- [1. PubTabNet数据集](#1-pubtabnet数据集)
-- [2. 好未来表格识别竞赛数据集](#2-好未来表格识别竞赛数据集)
-- [3. 好未来表格识别竞赛数据集](#2-WTW中文场景表格数据集)
-
-这里整理了常用表格识别数据集，持续更新中，欢迎各位小伙伴贡献数据集～
-
-## 数据集汇总
-
-| 数据集名称 |图片下载地址| PPOCR标注下载地址 |
-|---|---|---|
-| PubTabNet |https://github.com/ibm-aur-nlp/PubTabNet| jsonl格式，可直接用[pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)加载 |
-| 好未来表格识别竞赛数据集 |https://ai.100tal.com/dataset| jsonl格式，可直接用[pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)加载 |
-| WTW中文场景表格数据集 |https://github.com/wangwen-whu/WTW-Dataset| 需要进行转换后才能用[pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)加载 |
-
-## 1. PubTabNet数据集
-- **数据简介**：PubTabNet数据集的训练集合中包含50万张图像，验证集合中包含0.9万张图像。部分图像可视化如下所示。
-
-
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-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - 下载完成后，双击安装程序进入图形界面
- - 默认安装位置为C盘，建议将安装位置更改到D盘：
-
- 
- - 勾选conda加入环境变量，忽略警告：
-
- 
-
-#### 1.1.2 打开终端并创建conda环境
-
-- 打开Anaconda Prompt终端：左下角Windows Start Menu -> Anaconda3 -> Anaconda Prompt启动控制台
-
- 
-
-
-- 创建新的conda环境
-
- ```shell
- # 在命令行输入以下命令，创建名为paddle_env的环境
- # 此处为加速下载，使用清华源
- conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ # 这是一行命令
- ```
-
- 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
-
- 之后命令行中会输出提示信息，输入y并回车继续安装
-
- 
-
-- 激活刚创建的conda环境，在命令行中输入以下命令：
-
- ```shell
- # 激活paddle_env环境
- conda activate paddle_env
- # 查看当前python的位置
- where python
- ```
-
- 
-
-
-
-
-以上anaconda环境和python环境安装完毕
-
-
-
-### 1.2 Mac
-
-#### 1.2.1 安装Anaconda
-
-- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
- - Anaconda是1个常用的python包管理程序
- - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
-- Anaconda下载：
- - 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
-
- 
-
- - 选择最下方的`Anaconda3-2021.05-MacOSX-x86_64.pkg`下载
-- 下载完成后，双击.pkg文件进入图形界面
- - 按默认设置即可，安装需要花费一段时间
-- 建议安装vscode或pycharm等代码编辑器
-
-#### 1.2.2 打开终端并创建conda环境
-
-- 打开终端
-
- - 同时按下command键和空格键，在聚焦搜索中输入"终端"，双击进入终端
-
-- **将conda加入环境变量**
-
- - 加入环境变量是为了让系统能识别conda命令
-
- - 输入以下命令，在终端中打开`~/.bash_profile`：
-
- ```shell
- vim ~/.bash_profile
- ```
-
- - 在`~/.bash_profile`中将conda添加为环境变量：
-
- ```shell
- # 先按i进入编辑模式
- # 在第一行输入：
- export PATH="~/opt/anaconda3/bin:$PATH"
- # 若安装时自定义了安装位置，则将~/opt/anaconda3/bin改为自定义的安装目录下的bin文件夹
- ```
-
- ```shell
- # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
- export PATH="~/opt/anaconda3/bin:$PATH"
- # >>> conda initialize >>>
- # !! Contents within this block are managed by 'conda init' !!
- __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
- if [ $? -eq 0 ]; then
- eval "$__conda_setup"
- else
- if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
- . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
- else
- export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
- fi
- fi
- unset __conda_setup
- # <<< conda initialize <<<
- ```
-
- - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
-
- - 验证是否能识别conda命令：
-
- - 在终端中输入`source ~/.bash_profile`以更新环境变量
- - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
-
-- 创建新的conda环境
-
- ```shell
- # 在命令行输入以下命令，创建名为paddle_env的环境
- # 此处为加速下载，使用清华源
- conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- ```
-
- - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
-
- - 之后命令行中会输出提示信息，输入y并回车继续安装
-
- - 
-
-- 激活刚创建的conda环境，在命令行中输入以下命令：
-
- ```shell
- # 激活paddle_env环境
- conda activate paddle_env
- # 查看当前python的位置
- where python
- ```
-
- 
-
-以上anaconda环境和python环境安装完毕
-
-
-
-### 1.3 Linux
-
-Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker且需要训练PaddleOCR模型，推荐使用Docker环境，PaddleOCR的开发流程均在Docker环境下运行。如果你不熟悉Docker，也可以使用Anaconda来运行项目。
-
-#### 1.3.1 Anaconda环境配置
-
-- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
- - Anaconda是1个常用的python包管理程序
- - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
-
-- **下载Anaconda**：
-
- - 下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
- 
-
- - 选择适合您操作系统的版本
- - 可在终端输入`uname -m`查询系统所用的指令集
-
-- 下载法1：本地下载，再将安装包传到linux服务器上
-
-- 下载法2：直接使用linux命令行下载
-
- ```shell
- # 首先安装wget
- sudo apt-get install wget # Ubuntu
- sudo yum install wget # CentOS
- ```
-
- ```shell
- # 然后使用wget从清华源上下载
- # 如要下载Anaconda3-2021.05-Linux-x86_64.sh，则下载命令如下：
- wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
-
- # 若您要下载其他版本，需要将最后1个/后的文件名改成您希望下载的版本
- ```
-
-- 安装Anaconda：
-
- - 在命令行输入`sh Anaconda3-2021.05-Linux-x86_64.sh`
- - 若您下载的是其它版本，则将该命令的文件名替换为您下载的文件名
- - 按照安装提示安装即可
- - 查看许可时可输入q来退出
-
-- **将conda加入环境变量**
-
- - 加入环境变量是为了让系统能识别conda命令，若您在安装时已将conda加入环境变量path，则可跳过本步
-
- - 在终端中打开`~/.bashrc`：
-
- ```shell
- # 在终端中输入以下命令：
- vim ~/.bashrc
- ```
-
- - 在`~/.bashrc`中将conda添加为环境变量：
-
- ```shell
- # 先按i进入编辑模式
- # 在第一行输入：
- export PATH="~/anaconda3/bin:$PATH"
- # 若安装时自定义了安装位置，则将~/anaconda3/bin改为自定义的安装目录下的bin文件夹
- ```
-
- ```shell
- # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
- export PATH="~/opt/anaconda3/bin:$PATH"
- # >>> conda initialize >>>
- # !! Contents within this block are managed by 'conda init' !!
- __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
- if [ $? -eq 0 ]; then
- eval "$__conda_setup"
- else
- if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
- . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
- else
- export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
- fi
- fi
- unset __conda_setup
- # <<< conda initialize <<<
- ```
-
- - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
-
- - 验证是否能识别conda命令：
-
- - 在终端中输入`source ~/.bash_profile`以更新环境变量
- - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
-
-- 创建新的conda环境
-
- ```shell
- # 在命令行输入以下命令，创建名为paddle_env的环境
- # 此处为加速下载，使用清华源
- conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- ```
-
- - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
-
- - 之后命令行中会输出提示信息，输入y并回车继续安装
-
- 
-
-- 激活刚创建的conda环境，在命令行中输入以下命令：
-
- ```shell
- # 激活paddle_env环境
- conda activate paddle_env
- ```
-
-
-以上anaconda环境和python环境安装完毕
-
-#### 1.3.2 Docker环境配置
-
-**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。**
-
-```bash
-# 切换到工作目录下
-cd /home/Projects
-# 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
-# 创建一个名字为ppocr的docker容器，并将当前目录映射到容器的/paddle目录下
-
-#如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
-sudo docker run --name ppocr -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
-
-#如果使用CUDA10，请运行以下命令创建容器，设置docker容器共享内存shm-size为64G，建议设置32G以上
-# 如果是CUDA11+CUDNN8，推荐使用镜像registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda11.2-cudnn8
-sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
-
-# ctrl+P+Q可退出docker 容器，重新进入docker 容器使用如下命令
-sudo docker container exec -it ppocr /bin/bash
-```
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@@ -1,231 +0,0 @@
-# 模型微调
-
-## 1. 模型微调背景与意义
-
-PaddleOCR提供的PP-OCR系列模型在通用场景中性能优异，能够解决绝大多数情况下的检测与识别问题。在垂类场景中，如果希望获取更优的模型效果，可以通过模型微调的方法，进一步提升PP-OCR系列检测与识别模型的精度。
-
-本文主要介绍文本检测与识别模型在模型微调时的一些注意事项，最终希望您在自己的场景中，通过模型微调，可以获取精度更高的文本检测与识别模型。
-
-本文核心要点如下所示。
-
-1. PP-OCR提供的预训练模型有较好的泛化能力
-2. 加入少量真实数据（检测任务>=500张, 识别任务>=5000张），会大幅提升垂类场景的检测与识别效果
-3. 在模型微调时，加入真实通用场景数据，可以进一步提升模型精度与泛化性能
-4. 在图像检测任务中，增大图像的预测尺度，能够进一步提升较小文字区域的检测效果
-5. 在模型微调时，需要适当调整超参数（学习率，batch size最为重要），以获得更优的微调效果。
-
-更多详细内容，请参考第2章与第3章。
-
-## 2. 文本检测模型微调
-
-### 2.1 数据选择
-
-* 数据量：建议至少准备500张的文本检测数据集用于模型微调。
-
-* 数据标注：单行文本标注格式，建议标注的检测框与实际语义内容一致。如在火车票场景中，姓氏与名字可能离得较远，但是它们在语义上属于同一个检测字段，这里也需要将整个姓名标注为1个检测框。
-
-### 2.2 模型选择
-
-建议选择PP-OCRv3模型（配置文件：[ch_PP-OCRv3_det_student.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml)，预训练模型：[ch_PP-OCRv3_det_distill_train.tar](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)）进行微调，其精度与泛化性能是目前提供的最优预训练模型。
-
-更多PP-OCR系列模型，请参考[PP-OCR 系列模型库](./models_list.md)。
-
-注意：在使用上述预训练模型的时候，需要使用文件夹中的`student.pdparams`文件作为预训练模型，即，仅使用学生模型。
-
-
-### 2.3 训练超参选择
-
-在模型微调的时候，最重要的超参就是预训练模型路径`pretrained_model`, 学习率`learning_rate`与`batch_size`，部分配置文件如下所示。
-
-```yaml
-Global:
- pretrained_model: ./ch_PP-OCRv3_det_distill_train/student.pdparams # 预训练模型路径
-Optimizer:
- lr:
- name: Cosine
- learning_rate: 0.001 # 学习率
- warmup_epoch: 2
- regularizer:
- name: 'L2'
- factor: 0
-
-Train:
- loader:
- shuffle: True
- drop_last: False
- batch_size_per_card: 8 # 单卡batch size
- num_workers: 4
-```
-
-上述配置文件中，首先需要将`pretrained_model`字段指定为`student.pdparams`文件路径。
-
-PaddleOCR提供的配置文件是在8卡训练（相当于总的batch size是`8*8=64`）、且没有加载预训练模型情况下的配置文件，因此您的场景中，学习率与总的batch size需要对应线性调整，例如
-
-* 如果您的场景中是单卡训练，单卡batch_size=8，则总的batch_size=8，建议将学习率调整为`1e-4`左右。
-* 如果您的场景中是单卡训练，由于显存限制，只能设置单卡batch_size=4，则总的batch_size=4，建议将学习率调整为`5e-5`左右。
-
-### 2.4 预测超参选择
-
-对训练好的模型导出并进行推理时，可以通过进一步调整预测的图像尺度，来提升小面积文本的检测效果，下面是DBNet推理时的一些超参数，可以通过适当调整，提升效果。
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_db_thresh | float | 0.3 | DB输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 |
-| det_db_box_thresh | float | 0.6 | 检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 |
-| det_db_unclip_ratio | float | 1.5 | `Vatti clipping`算法的扩张系数，使用该方法对文字区域进行扩张 |
-| max_batch_size | int | 10 | 预测的batch size |
-| use_dilation | bool | False | 是否对分割结果进行膨胀以获取更优检测效果 |
-| det_db_score_mode | str | "fast" | DB的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
-
-
-更多关于推理方法的介绍可以参考[Paddle Inference推理教程](././inference_ppocr.md)。
-
-
-## 3. 文本识别模型微调
-
-
-### 3.1 数据选择
-
-* 数据量：不更换字典的情况下，建议至少准备5000张的文本识别数据集用于模型微调；如果更换了字典（不建议），需要的数量更多。
-
-* 数据分布：建议分布与实测场景尽量一致。如果实测场景包含大量短文本，则训练数据中建议也包含较多短文本，如果实测场景对于空格识别效果要求较高，则训练数据中建议也包含较多带空格的文本内容。
-
-* 数据合成：针对部分字符识别有误的情况，建议获取一批特定字符数据，加入到原数据中使用小学习率微调。其中原始数据与新增数据比例可尝试 10:1 ～ 5：1， 避免单一场景数据过多导致模型过拟合，同时尽量平衡语料词频，确保常用字的出现频率不会过低。
-
- 特定字符生成可以使用 TextRenderer 工具，合成例子可参考 [数码管数据合成](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/applications/%E5%85%89%E5%8A%9F%E7%8E%87%E8%AE%A1%E6%95%B0%E7%A0%81%E7%AE%A1%E5%AD%97%E7%AC%A6%E8%AF%86%E5%88%AB/%E5%85%89%E5%8A%9F%E7%8E%87%E8%AE%A1%E6%95%B0%E7%A0%81%E7%AE%A1%E5%AD%97%E7%AC%A6%E8%AF%86%E5%88%AB.md#31-%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87)
- ，合成数据语料尽量来自真实使用场景，在贴近真实场景的基础上保持字体、背景的丰富性，有助于提升模型效果。
-
-* 通用中英文数据：在训练的时候，可以在训练集中添加通用真实数据（如在不更换字典的微调场景中，建议添加LSVT、RCTW、MTWI等真实数据），进一步提升模型的泛化性能。
-
-### 3.2 模型选择
-
-建议选择PP-OCRv3模型（配置文件：[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)，预训练模型：[ch_PP-OCRv3_rec_train.tar](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar)）进行微调，其精度与泛化性能是目前提供的最优预训练模型。
-
-更多PP-OCR系列模型，请参考[PP-OCR 系列模型库](./models_list.md)。
-
-PP-OCRv3 模型使用了GTC策略，其中SAR分支参数量大，当训练数据为简单场景时模型容易过拟合，导致微调效果不佳，建议去除GTC策略，模型结构部分配置文件修改如下：
-
-```yaml
-Architecture:
- model_type: rec
- algorithm: SVTR
- Transform:
- Backbone:
- name: MobileNetV1Enhance
- scale: 0.5
- last_conv_stride: [1, 2]
- last_pool_type: avg
- Neck:
- name: SequenceEncoder
- encoder_type: svtr
- dims: 64
- depth: 2
- hidden_dims: 120
- use_guide: False
- Head:
- name: CTCHead
- fc_decay: 0.00001
-Loss:
- name: CTCLoss
-
-Train:
- dataset:
- ......
- transforms:
- # 去除 RecConAug 增广
- # - RecConAug:
- # prob: 0.5
- # ext_data_num: 2
- # image_shape: [48, 320, 3]
- # max_text_length: *max_text_length
- - RecAug:
- # 修改 Encode 方式
- - CTCLabelEncode:
- - KeepKeys:
- keep_keys:
- - image
- - label
- - length
-...
-
-Eval:
- dataset:
- ...
- transforms:
- ...
- - CTCLabelEncode:
- - KeepKeys:
- keep_keys:
- - image
- - label
- - length
-...
-
-
-```
-
-### 3.3 训练超参选择
-
-与文本检测任务微调相同，在识别模型微调的时候，最重要的超参就是预训练模型路径`pretrained_model`, 学习率`learning_rate`与`batch_size`，部分默认配置文件如下所示。
-
-```yaml
-Global:
- pretrained_model: # 预训练模型路径
-Optimizer:
- lr:
- name: Piecewise
- decay_epochs : [700, 800]
- values : [0.001, 0.0001] # 学习率
- warmup_epoch: 5
- regularizer:
- name: 'L2'
- factor: 0
-
-Train:
- dataset:
- name: SimpleDataSet
- data_dir: ./train_data/
- label_file_list:
- - ./train_data/train_list.txt
- ratio_list: [1.0] # 采样比例，默认值是[1.0]
- loader:
- shuffle: True
- drop_last: False
- batch_size_per_card: 128 # 单卡batch size
- num_workers: 8
-
-```
-
-
-上述配置文件中，首先需要将`pretrained_model`字段指定为3.2章节中解压得到的`ch_PP-OCRv3_rec_train/best_accuracy.pdparams`文件路径。
-
-PaddleOCR提供的配置文件是在8卡训练（相当于总的batch size是`8*128=1024`）、且没有加载预训练模型情况下的配置文件，因此您的场景中，学习率与总的batch size需要对应线性调整，例如：
-
-* 如果您的场景中是单卡训练，单卡batch_size=128，则总的batch_size=128，在加载预训练模型的情况下，建议将学习率调整为`[1e-4, 2e-5]`左右（piecewise学习率策略，需设置2个值，下同）。
-* 如果您的场景中是单卡训练，因为显存限制，只能设置单卡batch_size=64，则总的batch_size=64，在加载预训练模型的情况下，建议将学习率调整为`[5e-5, 1e-5]`左右。
-
-
-如果有通用真实场景数据加进来，建议每个epoch中，垂类场景数据与真实场景的数据量保持在1:1左右。
-
-比如：您自己的垂类场景识别数据量为1W，数据标签文件为`vertical.txt`，收集到的通用场景识别数据量为10W，数据标签文件为`general.txt`，
-
-
-那么，可以设置`label_file_list`和`ratio_list`参数如下所示。每个epoch中，`vertical.txt`中会进行全采样（采样比例为1.0），包含1W条数据；`general.txt`中会按照0.1的采样比例进行采样，包含`10W*0.1=1W`条数据，最终二者的比例为`1:1`。
-
-```yaml
-Train:
- dataset:
- name: SimpleDataSet
- data_dir: ./train_data/
- label_file_list:
- - vertical.txt
- - general.txt
- ratio_list: [1.0, 0.1]
-```
-
-
-### 3.4 训练调优
-
-训练过程并非一蹴而就的，完成一个阶段的训练评估后，建议收集分析当前模型在真实场景中的 badcase，有针对性的调整训练数据比例，或者进一步新增合成数据。通过多次迭代训练，不断优化模型效果。
-
-如果在训练时修改了自定义字典，由于无法加载最后一层FC的参数，在迭代初期acc=0是正常的情况，不必担心，加载预训练模型依然可以加快模型收敛。
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diff --git a/doc/doc_ch/hardware/install_other_devices.md b/doc/doc_ch/hardware/install_other_devices.md
deleted file mode 100644
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@@ -1,44 +0,0 @@
-# 多硬件安装飞桨
-本文档主要针对昇腾 NPU 硬件平台，介绍如何安装飞桨。
-## 1. 昇腾 NPU 飞桨安装
-### 1.1 环境准备
-当前 PaddleOCR 支持昇腾 910B 芯片，昇腾驱动版本为 23.0.3。考虑到环境差异性，我们推荐使用飞桨官方提供的标准镜像完成环境准备。
-- 1. 拉取镜像，此镜像仅为开发环境，镜像中不包含预编译的飞桨安装包，镜像中已经默认安装了昇腾算子库 CANN-8.0.RC1。
-
-```
-# 适用于 X86 架构，暂时不提供 Arch64 架构镜像
-docker pull registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39
-```
-
-- 2. 参考如下命令启动容器，ASCEND_RT_VISIBLE_DEVICES 指定可见的 NPU 卡号
-```
-docker run -it --name paddle-npu-dev -v $(pwd):/work \
- --privileged --network=host --shm-size=128G -w=/work \
- -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
- -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
- -v /usr/local/dcmi:/usr/local/dcmi \
- -e ASCEND_RT_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" \
- registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39 /bin/bash
-```
-### 1.2 安装 paddle 包
-当前提供 Python3.9 的 wheel 安装包。如有其他 Python 版本需求，可以参考[飞桨官方文档](https://www.paddlepaddle.org.cn/install/quick)自行编译安装。
-
-- 1. 下载安装 Python3.9 的 wheel 安装包
-
-```
-# 注意需要先安装飞桨 cpu 版本
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddle-device/npu/paddlepaddle-0.0.0-cp39-cp39-linux_x86_64.whl
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddle-device/npu/paddle_custom_npu-0.0.0-cp39-cp39-linux_x86_64.whl
-```
-- 2. 验证安装包
-安装完成之后，运行如下命令。
-```
-python -c "import paddle; paddle.utils.run_check()"
-```
-预期得到如下输出结果
-```
-Running verify PaddlePaddle program ...
-PaddlePaddle works well on 1 npu.
-PaddlePaddle works well on 8 npus.
-PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
-```
diff --git a/doc/doc_ch/hardware/supported_models.md b/doc/doc_ch/hardware/supported_models.md
deleted file mode 100644
index 10b5d240d5..0000000000
--- a/doc/doc_ch/hardware/supported_models.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# PaddleOCR模型列表
-
-*多硬件安装方式请参考[多硬件安装文档](install_other_devices.md)*
-
-| 模型名称 | 昇腾NPU |
-| ---------------- | -------- |
-| PP-OCRv4 | √ |
diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
deleted file mode 100644
index b73245a410..0000000000
--- a/doc/doc_ch/inference.md
+++ /dev/null
@@ -1,405 +0,0 @@
-
-# 基于Python预测引擎的推理
-
-推理模型（通过 paddle.jit.save 保存的模型）一般是模型训练完成后保存的固化模型，多用于部署中的预测。
-
-训练过程中保存的模型是检查点模型，保存的是模型的参数，多用于恢复训练。
-
-与检查点模型相比，推理模型会额外保存模型的结构信息。因此，它更容易部署，因为模型结构和模型参数已经固化在推理模型文件中，适合与实际系统集成。更多细节请参考文档[Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/docs/zh_CN/extension/paddle_mobile_inference.md).
-
-接下来，我们首先介绍如何将训练好的模型转换为推理模型，然后介绍基于推理模型的文本检测、文本识别、角度分类及其串联。
-
-- [基于Python预测引擎的推理](#基于python预测引擎的推理)
- - [1. 将训练模型转换为推理模型](#1-将训练模型转换为推理模型)
- - [1.1 将检测模型转换为推理模型](#11-将检测模型转换为推理模型)
- - [1.2 将识别模型转换为推理模型](#12-将识别模型转换为推理模型)
- - [1.3 将角度分类模型转换为推理模型](#13-将角度分类模型转换为推理模型)
- - [2. 文本检测模型推理](#2-文本检测模型推理)
- - [2.1 轻量级中文检测模型推理](#21-轻量级中文检测模型推理)
- - [2.2 DB文本检测模型推理](#22-db文本检测模型推理)
- - [2.3 EAST文本检测模型推理](#23-east文本检测模型推理)
- - [2.4 Sast文本检测模型推理](#24-sast文本检测模型推理)
- - [(1). 四边形文本检测模型 (ICDAR2015)](#1-四边形文本检测模型-icdar2015)
- - [(2). 曲线文本检测模型 (Total-Text)](#2-曲线文本检测模型-total-text)
- - [3. 文本识别模型推理](#3-文本识别模型推理)
- - [3.1 轻量级中文文本识别模型推理](#31-轻量级中文文本识别模型推理)
- - [3.2 基于CTC的文本识别模型推理](#32-基于ctc的文本识别模型推理)
- - [3.3 基于SRN的文本识别模型推理](#33-基于srn的文本识别模型推理)
- - [3.4 使用自定义字符字典的文本识别模型推理](#34-使用自定义字符字典的文本识别模型推理)
- - [3.5 多语言模型推理](#35-多语言模型推理)
- - [4. 角度分类模型推理](#4-角度分类模型推理)
- - [5. 文本检测、角度分类与识别推理串联](#5-文本检测角度分类与识别推理串联)
- - [5.1 轻量级中文模型](#51-轻量级中文模型)
- - [5.2 其他模型](#52-其他模型)
-
-
-## 1. 将训练模型转换为推理模型
-
-
-### 1.1 将检测模型转换为推理模型
-
-下载轻量级中文检测模型：
-```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
-```
-
-上述模型是使用 MobileNetV3 作为backbone训练的 DB 算法。要将训练好的模型转换为推理模型，只需运行以下命令：
-```
-# -c Set the training algorithm yml configuration file
-# -o Set optional parameters
-# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.save_inference_dir Set the address where the converted model will be saved.
-
-python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/
-```
-
-在转换为推理模型时，使用的配置文件与训练时使用的配置文件相同。此外，还需要在配置文件中设置 `Global.pretrained_model` 参数。
-转换成功后，模型保存目录中会有三个文件：
-```
-inference/det_db/
- ├── inference.pdiparams # 检测推理模型的参数文件
- ├── inference.pdiparams.info # 检测推理模型的参数信息文件，可以忽略
- └── inference.pdmodel # 检测推理模型的程序文件
-```
-
-
-### 1.2 将识别模型转换为推理模型
-
-下载轻量级中文识别模型：
-```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
-```
-
-识别模型的转换方式与检测模型相同，如下所示：
-```
-# -c 设置训练算法的 yml 配置文件
-# -o 设置可选参数
-# Global.pretrained_model 参数设置要转换的训练模型地址，不需要添加文件后缀 .pdmodel, .pdopt 或 .pdparams。
-# Global.save_inference_dir 设置转换后模型保存的地址。
-
-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
-```
-
-如果您使用自己的数据集训练了一个模型，并且使用了不同的字典文件，请确保在配置文件中将 `character_dict_path` 修改为您的字典文件路径。
-
-转换成功后，模型保存目录中会有三个文件：
-```
-inference/det_db/
- ├── inference.pdiparams # The parameter file of recognition inference model
- ├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
- └── inference.pdmodel # The program file of recognition model
-```
-
-
-### 1.3 将角度分类模型转换为推理模型
-
-下载角度分类模型：
-```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
-```
-
-角度分类模型的转换方式与检测模型相同，如下所示：
-```
-# -c Set the training algorithm yml configuration file
-# -o Set optional parameters
-# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.save_inference_dir Set the address where the converted model will be saved.
-
-python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.save_inference_dir=./inference/cls/
-```
-
-转换成功后，目录中会有两个文件：
-```
-inference/det_db/
- ├── inference.pdiparams # The parameter file of angle class inference model
- ├── inference.pdiparams.info # The parameter information of angle class inference model, which can be ignored
- └── inference.pdmodel # The program file of angle class model
-```
-
-
-
-## 2. 文本检测模型推理
-
-以下将介绍轻量级中文检测模型推理、DB文本检测模型推理和EAST文本检测模型推理。默认配置基于DB文本检测模型的推理设置。
-由于EAST和DB算法有很大的不同，在推理时，需要**通过传入相应的参数来适配EAST文本检测算法**。
-
-
-### 2.1 轻量级中文检测模型推理
-
-对于轻量级中文检测模型推理，可以执行以下命令：
-
-```
-# download DB text detection inference model
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# predict
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
-```
-
-可视化的文本检测结果默认保存在 `./inference_results` 文件夹中，结果文件的名称以 `det_res` 为前缀。结果示例如下：
-
-
-
-可以使用参数 `limit_type` 和 `det_limit_side_len` 来限制输入图像的大小，
-`limit_type` 的可选参数是 [`max`, `min`]，
-而 `det_limit_side_len` 是一个正整数，通常设置为32的倍数，例如960。
-
-这些参数的默认设置是 `limit_type='max', det_limit_side_len=960`。表示网络输入图像的最长边不能超过960，
-如果超过这个值，图像将按相同比例调整尺寸，以确保最长边为 `det_limit_side_len`。
-设置为 `limit_type='min', det_limit_side_len=960`，则表示图像的最短边被限制为960。
-
-如果输入图片的分辨率较大，并且希望使用更大的分辨率进行预测，可以将 `det_limit_side_len` 设置为所需的值，例如1216：
-
-```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
-```
-
-如果希望使用CPU进行预测，请执行以下命令：
-```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
-```
-
-
-### 2.2 DB文本检测模型推理
-
-首先，将DB文本检测训练过程中保存的模型转换为推理模型。以基于Resnet50_vd骨干网络并在ICDAR2015英文数据集上训练的模型为例（[模型下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)），可以使用以下命令进行转换：
-
-```
-python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db
-```
-
-DB文本检测模型推理，可以执行以下命令：
-
-```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
-```
-
-可视化的文本检测结果默认保存在 `./inference_results` 文件夹中，结果文件的名称以 `det_res` 为前缀。结果示例如下：
-
-
-
-**注意**：由于ICDAR2015数据集只有1000张训练图像，主要针对英文场景，上述模型在中文文本图像上的检测结果非常差。
-
-
-
-### 2.3 EAST文本检测模型推理
-
-首先，将EAST文本检测训练过程中保存的模型转换为推理模型。以基于Resnet50_vd骨干网络并在ICDAR2015英文数据集上训练的模型为例（[模型下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)），可以使用以下命令进行转换：
-
-```
-python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_east
-```
-**对于EAST文本检测模型推理，需要设置参数 `--det_algorithm="EAST"`**，运行以下命令：
-
-```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
-```
-
-可视化的文本检测结果默认保存在 `./inference_results` 文件夹中，结果文件的名称以 `det_res` 为前缀。结果示例如下：
-
-
-
-**注意**：EAST后处理的局部感知NMS有两个版本：Python和C++。C++版本的速度明显快于Python版本。由于C++版本NMS的编译版本问题，只有在Python 3.5环境中才会调用C++版本NMS，在其他情况下将调用Python版本NMS。
-
-
-### 2.4 Sast文本检测模型推理
-
-#### (1). 四边形文本检测模型（ICDAR2015）
-
-首先，将SAST文本检测训练过程中保存的模型转换为推理模型。以基于Resnet50_vd骨干网络并在ICDAR2015英文数据集上训练的模型为例（[模型下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)），可以使用以下命令进行转换：
-
-```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_ic15
-```
-
-**对于SAST四边形文本检测模型推理，需要设置参数 `--det_algorithm="SAST"`**，运行以下命令：
-
-```
-python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
-```
-
-可视化的文本检测结果默认保存在 `./inference_results` 文件夹中，结果文件的名称以 `det_res` 为前缀。结果示例如下：
-
-
-
-#### (2). 曲线文本检测模型（Total-Text）
-
-首先，将SAST文本检测训练过程中保存的模型转换为推理模型。以基于Resnet50_vd骨干网络并在Total-Text英文数据集上训练的模型为例（[模型下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)），可以使用以下命令进行转换：
-
-```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt
-```
-
-对于SAST曲线文本检测模型推理，需要设置参数 `--det_algorithm="SAST"` 和 `--det_sast_polygon=True`，运行以下命令：
-
-```
-python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
-```
-
-可视化的文本检测结果默认保存在 `./inference_results` 文件夹中，结果文件的名称以 `det_res` 为前缀。结果示例如下：
-
-
-
-**注意**：SAST后处理的局部感知NMS有两个版本：Python和C++。C++版本的速度明显快于Python版本。由于C++版本NMS的编译版本问题，只有在Python 3.5环境中才会调用C++版本NMS，在其他情况下将调用Python版本NMS。
-
-
-## 3. 文本识别模型推理
-
-以下将介绍轻量级中文识别模型推理、其他基于CTC和Attention的文本识别模型推理。对于中文文本识别，建议选择基于CTC损失的识别模型。实践中也发现，基于Attention损失的模型效果不如基于CTC损失的模型。此外，如果在训练过程中修改了字符字典，请确保在推理过程中使用相同的字符集。详情请见下文。
-
-
-### 3.1 轻量级中文文本识别模型推理
-
-对于轻量级中文识别模型推理，可以执行以下命令：
-
-```
-# download CRNN text recognition inference model
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
-```
-
-
-
-执行命令后，上述图像的预测结果（识别的文本和分数）将打印在屏幕上。
-
-```bash
-Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
-```
-
-
-### 3.2 基于CTC的文本识别模型推理
-
-以CRNN为例，介绍基于CTC损失的识别模型推理。Rosetta和Star-Net的使用方式类似，无需设置识别算法参数 `rec_algorithm`。
-
-首先，将CRNN文本识别训练过程中保存的模型转换为推理模型。以基于Resnet34_vd骨干网络，使用MJSynth和SynthText（两个英文文本识别合成数据集）进行训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)）。可以按如下方式进行转换：
-
-```
-python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
-```
-
-对于CRNN文本识别模型推理，执行以下命令：
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
-```
-
-
-
-执行命令后，上述图像的识别结果如下：
-
-```bash
-Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
-```
-
-**注意**：由于上述模型参考了 [DTRB](https://arxiv.org/abs/1904.01906) 文本识别的训练和评估过程，与轻量级中文识别模型的训练在两个方面有所不同：
-
-- **训练时使用的图像分辨率不同**：上述模型训练时使用的图像分辨率为 [3，32，100]，而我们的中文模型训练时，为了确保对长文本的识别效果，使用的图像分辨率为 [3, 32, 320]。推理阶段的默认形状参数是训练阶段使用的图像分辨率，即 [3, 32, 320]。因此，在此处运行上述英文模型的推理时，需要通过参数 `rec_image_shape` 设置识别图像的形状。
-
-- **字符列表**：DTRB 论文中的实验仅针对 26 个小写英文字母和 10 个数字，共 36 个字符。所有大写和小写字符都转换为小写字符，不在上述列表中的字符被忽略并视为空格。因此，这里不使用字符字典文件，而是通过以下命令生成字典。
-
-```
-self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-dict_character = list(self.character_str)
-```
-
-
-### 3.3 基于SRN的文本识别模型推理
-
-基于SRN的识别模型需要额外设置识别算法参数 `--rec_algorithm="SRN"`。同时，需要确保预测形状与训练一致，例如：`--rec_image_shape="1, 64, 256"`。
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
- --rec_model_dir="./inference/srn/" \
- --rec_image_shape="1, 64, 256" \
- --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
- --rec_algorithm="SRN"
-```
-
-
-### 3.4 使用自定义字符字典的文本识别模型推理
-
-如果在训练过程中修改了文本字典，在使用推理模型进行预测时，需要通过 `--rec_char_dict_path` 指定字典路径。
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
-```
-
-
-
-### 3.5 多语言模型推理
-
-如果需要预测其他语言的模型，在使用推理模型进行预测时，需要通过 `--rec_char_dict_path` 指定字典路径。同时，为了获得正确的可视化结果，需要通过 `--vis_font_path` 指定可视化字体路径。在 `doc/fonts` 路径下默认提供了一些小语种字体，例如韩文识别：
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
-```
-
-
-执行命令后，上述图像的预测结果是：
-
-``` text
-Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
-```
-
-
-## 4. 角度分类模型推理
-
-对于角度分类模型推理，可以执行以下命令：
-
-```
-python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
-```
-```
-# download text angle class inference model：
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
-tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
-python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
-```
-
-
-执行命令后，上述图像的预测结果（分类角度和分数）将打印在屏幕上。
-
-```
- Predicts of ./doc/imgs_words_en/word_10.png:['0', 0.9999995]
-```
-
-## 5. 文本检测、角度分类和识别推理串联
-
-
-### 5.1 轻量级中文模型
-
-在进行预测时，需要通过参数 `image_dir` 指定单张图像或图像文件夹的路径，参数 `det_model_dir` 指定检测推理模型的路径，参数 `cls_model_dir` 指定角度分类推理模型的路径，参数 `rec_model_dir` 指定识别推理模型的路径。参数 `use_angle_cls` 用于控制是否启用角度分类模型。参数 `use_mp` 指定是否使用多进程推理，`total_process_num` 指定使用多进程时的进程数量（Paddle Inference 不是线程安全的，建议使用多进程）。可视化的识别结果默认保存在 `./inference_results` 文件夹中。
-
-```shell
-# use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
-
-# not use use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
-
-# use multi-process
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
-```
-
-
-执行命令后，识别结果图像如下：
-
-
-
-
-### 5.2 其他模型
-
-如果你想尝试其他检测算法或识别算法，请参考上述文本检测模型推理和文本识别模型推理，更新相应的配置和模型。
-
-**注意：由于检测框旋转逻辑的限制，目前不支持将 SAST 曲线文本检测模型（使用参数 `det_sast_polygon=True`）与其他模型组合使用。**
-
-以下命令使用 EAST 文本检测和 STAR-Net 文本识别的组合：
-
-```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
-```
-
-执行命令后，识别结果图像如下：
-
-
diff --git a/doc/doc_ch/inference_args.md b/doc/doc_ch/inference_args.md
deleted file mode 100644
index aad7973b16..0000000000
--- a/doc/doc_ch/inference_args.md
+++ /dev/null
@@ -1,121 +0,0 @@
-# PaddleOCR模型推理参数解释
-
-在使用PaddleOCR进行模型推理时，可以自定义修改参数，来修改模型、数据、预处理、后处理等内容（参数文件：[utility.py](../../tools/infer/utility.py)），详细的参数解释如下所示。
-
-* 全局信息
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| image_dir | str | 无，必须显式指定 | 图像或者文件夹路径 |
-| page_num | int | 0 | 当输入类型为pdf文件时有效，指定预测前面page_num页，默认预测所有页 |
-| vis_font_path | str | "./doc/fonts/simfang.ttf" | 用于可视化的字体路径 |
-| drop_score | float | 0.5 | 识别得分小于该值的结果会被丢弃，不会作为返回结果 |
-| use_pdserving | bool | False | 是否使用Paddle Serving进行预测 |
-| warmup | bool | False | 是否开启warmup，在统计预测耗时的时候，可以使用这种方法 |
-| draw_img_save_dir | str | "./inference_results" | 系统串联预测OCR结果的保存文件夹 |
-| save_crop_res | bool | False | 是否保存OCR的识别文本图像 |
-| crop_res_save_dir | str | "./output" | 保存OCR识别出来的文本图像路径 |
-| use_mp | bool | False | 是否开启多进程预测 |
-| total_process_num | int | 6 | 开启的进程数，`use_mp`为`True`时生效 |
-| process_id | int | 0 | 当前进程的id号，无需自己修改 |
-| benchmark | bool | False | 是否开启benchmark，对预测速度、显存占用等进行统计 |
-| save_log_path | str | "./log_output/" | 开启`benchmark`时，日志结果的保存文件夹 |
-| show_log | bool | True | 是否显示预测中的日志信息 |
-| use_onnx | bool | False | 是否开启onnx预测 |
-
-
-* 预测引擎相关
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| use_gpu | bool | True | 是否使用GPU进行预测 |
-| ir_optim | bool | True | 是否对计算图进行分析与优化，开启后可以加速预测过程 |
-| use_tensorrt | bool | False | 是否开启tensorrt |
-| min_subgraph_size | int | 15 | tensorrt中最小子图size，当子图的size大于该值时，才会尝试对该子图使用trt engine计算 |
-| precision | str | fp32 | 预测的精度，支持`fp32`, `fp16`, `int8` 3种输入 |
-| enable_mkldnn | bool | True | 是否开启mkldnn |
-| cpu_threads | int | 10 | 开启mkldnn时，cpu预测的线程数 |
-
-* 文本检测模型相关
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_algorithm | str | "DB" | 文本检测算法名称，目前支持`DB`, `EAST`, `SAST`, `PSE`, `DB++`, `FCE` |
-| det_model_dir | str | xx | 检测inference模型路径 |
-| det_limit_side_len | int | 960 | 检测的图像边长限制 |
-| det_limit_type | str | "max" | 检测的边长限制类型，目前支持`min`和`max`，`min`表示保证图像最短边不小于`det_limit_side_len`，`max`表示保证图像最长边不大于`det_limit_side_len` |
-
-其中，DB算法相关参数如下
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_db_thresh | float | 0.3 | DB输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 |
-| det_db_box_thresh | float | 0.6 | 检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 |
-| det_db_unclip_ratio | float | 1.5 | `Vatti clipping`算法的扩张系数，使用该方法对文字区域进行扩张 |
-| max_batch_size | int | 10 | 预测的batch size |
-| use_dilation | bool | False | 是否对分割结果进行膨胀以获取更优检测效果 |
-| det_db_score_mode | str | "fast" | DB的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
-
-EAST算法相关参数如下
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_east_score_thresh | float | 0.8 | EAST后处理中score map的阈值 |
-| det_east_cover_thresh | float | 0.1 | EAST后处理中文本框的平均得分阈值 |
-| det_east_nms_thresh | float | 0.2 | EAST后处理中nms的阈值 |
-
-SAST算法相关参数如下
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_sast_score_thresh | float | 0.5 | SAST后处理中的得分阈值 |
-| det_sast_nms_thresh | float | 0.5 | SAST后处理中nms的阈值 |
-| det_box_type | str | quad | 是否多边形检测，弯曲文本场景（如Total-Text）设置为'poly' |
-
-PSE算法相关参数如下
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| det_pse_thresh | float | 0.0 | 对输出图做二值化的阈值 |
-| det_pse_box_thresh | float | 0.85 | 对box进行过滤的阈值，低于此阈值的丢弃 |
-| det_pse_min_area | float | 16 | box的最小面积，低于此阈值的丢弃 |
-| det_box_type | str | "quad" | 返回框的类型，quad:四点坐标，poly: 弯曲文本的所有点坐标 |
-| det_pse_scale | int | 1 | 输入图像相对于进后处理的图的比例，如`640*640`的图像，网络输出为`160*160`，scale为2的情况下，进后处理的图片shape为`320*320`。这个值调大可以加快后处理速度，但是会带来精度的下降 |
-
-* 文本识别模型相关
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| rec_algorithm | str | "CRNN" | 文本识别算法名称，目前支持`CRNN`, `SRN`, `RARE`, `NETR`, `SAR`, `ViTSTR`, `ABINet`, `VisionLAN`, `SPIN`, `RobustScanner`, `SVTR`, `SVTR_LCNet` |
-| rec_model_dir | str | 无，如果使用识别模型，该项是必填项 | 识别inference模型路径 |
-| rec_image_shape | str | "3,48,320" | 识别时的图像尺寸 |
-| rec_batch_num | int | 6 | 识别的batch size |
-| max_text_length | int | 25 | 识别结果最大长度，在`SRN`中有效 |
-| rec_char_dict_path | str | "./ppocr/utils/ppocr_keys_v1.txt" | 识别的字符字典文件 |
-| use_space_char | bool | True | 是否包含空格，如果为`True`，则会在最后字符字典中补充`空格`字符 |
-
-
-* 端到端文本检测与识别模型相关
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| e2e_algorithm | str | "PGNet" | 端到端算法名称，目前支持`PGNet` |
-| e2e_model_dir | str | 无，如果使用端到端模型，该项是必填项 | 端到端模型inference模型路径 |
-| e2e_limit_side_len | int | 768 | 端到端的输入图像边长限制 |
-| e2e_limit_type | str | "max" | 端到端的边长限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`e2e_limit_side_len`，`max`表示保证图像最长边不大于`e2e_limit_side_len` |
-| e2e_pgnet_score_thresh | float | 0.5 | 端到端得分阈值，小于该阈值的结果会被丢弃 |
-| e2e_char_dict_path | str | "./ppocr/utils/ic15_dict.txt" | 识别的字典文件路径 |
-| e2e_pgnet_valid_set | str | "totaltext" | 验证集名称，目前支持`totaltext`, `partvgg`，不同数据集对应的后处理方式不同，与训练过程保持一致即可 |
-| e2e_pgnet_mode | str | "fast" | PGNet的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
-
-
-* 方向分类器模型相关
-
-| 参数名称 | 类型 | 默认值 | 含义 |
-| :--: | :--: | :--: | :--: |
-| use_angle_cls | bool | False | 是否使用方向分类器 |
-| cls_model_dir | str | 无，如果需要使用，则必须显式指定路径 | 方向分类器inference模型路径 |
-| cls_image_shape | str | "3,48,192" | 预测尺度 |
-| label_list | list | ['0', '180'] | class id对应的角度值 |
-| cls_batch_num | int | 6 | 方向分类器预测的batch size |
-| cls_thresh | float | 0.9 | 预测阈值，模型预测结果为180度，且得分大于该阈值时，认为最终预测结果为180度，需要翻转 |
diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md
deleted file mode 100644
index 085802fc2c..0000000000
--- a/doc/doc_ch/inference_ppocr.md
+++ /dev/null
@@ -1,194 +0,0 @@
-# 基于Python引擎的PP-OCR模型库推理
-
-本文介绍针对PP-OCR模型库的Python推理引擎使用方法，内容依次为文本检测、文本识别、方向分类器以及三者串联在CPU、GPU上的预测方法。
-
-
-- [基于Python引擎的PP-OCR模型库推理](#基于python引擎的pp-ocr模型库推理)
- - [1. 文本检测模型推理](#1-文本检测模型推理)
- - [2. 文本识别模型推理](#2-文本识别模型推理)
- - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理)
- - [2.2 英文识别模型推理](#22-英文识别模型推理)
- - [2.3 多语言模型的推理](#23-多语言模型的推理)
- - [3. 方向分类模型推理](#3-方向分类模型推理)
- - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理)
- - [5. TensorRT推理](#5-tensorrt推理)
-
-
-
-## 1. 文本检测模型推理
-
-文本检测模型推理，默认使用DB模型的配置参数。超轻量中文检测模型推理，可以执行如下命令：
-
-```
-# 下载超轻量中文检测模型：
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
-tar xf ch_PP-OCRv3_det_infer.tar
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/"
-
-```
-
-可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
-
-
-
-通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制，
-`limit_type`可选参数为[`max`, `min`]，
-`det_limit_size_len` 为正整数，一般设置为32 的倍数，比如960。
-
-参数默认设置为`limit_type='max', det_limit_side_len=960`。表示网络输入图像的最长边不能超过960，
-如果超过这个值，会对图像做等宽比的resize操作，确保最长边为`det_limit_side_len`。
-设置为`limit_type='min', det_limit_side_len=960` 则表示限制图像的最短边为960。
-
-如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
-
-```bash
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --det_limit_type=max --det_limit_side_len=1216
-```
-
-如果想使用CPU进行预测，执行命令如下
-
-```bash
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --use_gpu=False
-```
-
-
-
-
-## 2. 文本识别模型推理
-
-
-
-### 2.1 超轻量中文识别模型推理
-
-**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 如果使用其他识别模型，则需根据模型设置参数`--rec_image_shape`。此外，`PP-OCRv3`的识别模型默认使用的`rec_algorithm`为`SVTR_LCNet`，注意和原始`SVTR`的区别。
-
-超轻量中文识别模型推理，可以执行如下命令：
-
-```
-# 下载超轻量中文识别模型：
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
-tar xf ch_PP-OCRv3_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv3_rec_infer/"
-```
-
-
-
-执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
-
-```bash
-Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379)
-```
-
-
-
-### 2.2 英文识别模型推理
-
-英文识别模型推理，可以执行如下命令， 注意修改字典路径：
-
-```
-# 下载英文数字识别模型：
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar
-tar xf en_PP-OCRv3_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_rec_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
-```
-
-
-
-执行命令后，上图的预测结果为：
-
-```
-Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
-```
-
-
-### 2.3 多语言模型的推理
-
-如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
-```
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
-```
-
-
-
-执行命令后，上图的预测结果为：
-
-``` text
-Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
-```
-
-
-
-## 3. 方向分类模型推理
-
-方向分类模型推理，可以执行如下命令：
-
-```
-# 下载超轻量中文方向分类器模型：
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
-tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
-python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
-```
-
-
-
-执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
-
-```
-Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
-```
-
-
-
-## 4. 文本检测、方向分类和文字识别串联推理
-
-**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 如果使用其他识别模型，则需根据模型设置参数`--rec_image_shape`。此外，`PP-OCRv3`的识别模型默认使用的`rec_algorithm`为`SVTR_LCNet`，注意和原始`SVTR`的区别。
-
-以超轻量中文OCR模型推理为例，在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径，也支持PDF文件、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。`use_mp`表示是否使用多进程（Paddle Inference并不是线程安全，建议使用多进程）。`total_process_num`表示在使用多进程时的进程数。可视化识别结果默认保存到 ./inference_results 文件夹里面。
-
-```shell
-# 使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true
-# 不使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false
-# 使用多进程
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
-# 使用PDF文件,可以通过使用`page_num`参数来控制推理前几页，默认为0，表示推理所有页
-python3 tools/infer/predict_system.py --image_dir="./xxx.pdf" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true --page_num=2
-```
-
-
-执行命令后，识别结果图像如下：
-
-
-
-更多关于推理超参数的配置与解释，请参考：[模型推理超参数解释教程](./inference_args.md)。
-
-
-## 5. TensorRT推理
-
-Paddle Inference 采用子图的形式集成 TensorRT，针对 GPU 推理场景，TensorRT 可对一些子图进行优化，包括 OP 的横向和纵向融合，过滤冗余的 OP，并为 OP 自动选择最优的 kernel，加快推理速度。
-
-如果希望使用Paddle Inference进行TRT推理，一般需要2个步骤。
-
-* （1）收集该模型关于特定数据集的动态shape信息，并存储到文件中。
-* （2）加载动态shape信息文件，进行TRT推理。
-
-以文本检测模型为例，首先使用下面的命令，生成动态shape文件，最终会在`ch_PP-OCRv3_det_infer`目录下面生成`det_trt_dynamic_shape.txt`的文件，该文件即存储了动态shape信息的文件。
-
-```bash
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --use_tensorrt=True
-```
-
-上面的推理过程仅用于收集动态shape信息，没有用TRT进行推理。
-
-运行完成以后，再使用下面的命令，进行TRT推理。
-
-```bash
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --use_tensorrt=True
-```
-
-**注意：**
-
-* 如果在第一步中，已经存在动态shape信息文件，则无需重新收集，直接预测，即使用TRT推理；如果希望重新生成动态shape信息文件，则需要先将模型目录下的动态shape信息文件删掉，再重新生成。
-* 动态shape信息文件一般情况下仅需生成一次。在实际部署过程中，建议首先在线下验证集或者测试集合上生成好，之后可以直接加载该文件进行线上TRT推理。
diff --git a/doc/doc_ch/installation.md b/doc/doc_ch/installation.md
deleted file mode 100644
index 962a926911..0000000000
--- a/doc/doc_ch/installation.md
+++ /dev/null
@@ -1,62 +0,0 @@
-## 快速安装
-
-经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
-PaddleOCR 工作环境
-- PaddlePaddle 2.0.0
-- python3.7
-- glibc 2.23
-- cuDNN 7.6+ (GPU)
-
-建议使用我们提供的docker运行PaddleOCR，有关docker、nvidia-docker使用请参考[链接](https://www.runoob.com/docker/docker-tutorial.html/)。
-
-*如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。*
-
-**1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。**
-```
-# 切换到工作目录下
-cd /home/Projects
-# 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
-# 创建一个名字为ppocr的docker容器，并将当前目录映射到容器的/paddle目录下
-
-如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
-sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
-
-如果使用CUDA10，请运行以下命令创建容器，设置docker容器共享内存shm-size为64G，建议设置32G以上
-sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
-
-您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
-
-# ctrl+P+Q可退出docker 容器，重新进入docker 容器使用如下命令
-sudo docker container exec -it ppocr /bin/bash
-```
-
-**2. 安装PaddlePaddle 2.0**
-```
-pip3 install --upgrade pip
-
-如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
-python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
-
-如果您的机器是CPU，请运行以下命令安装
-
-python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
-
-更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
-```
-
-**3. 克隆PaddleOCR repo代码**
-```
-【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
-
-如果因为网络问题无法pull成功，也可选择使用码云上的托管：
-
-git clone https://gitee.com/paddlepaddle/PaddleOCR
-
-注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
-```
-
-**4. 安装第三方库**
-```
-cd PaddleOCR
-pip3 install -r requirements.txt
-```
diff --git a/doc/doc_ch/kie.md b/doc/doc_ch/kie.md
deleted file mode 100644
index 0ebb05d0e1..0000000000
--- a/doc/doc_ch/kie.md
+++ /dev/null
@@ -1,507 +0,0 @@
-# 关键信息抽取
-
-本文提供了PaddleOCR关键信息抽取的全流程指南，包括语义实体识别 (Semantic Entity Recognition) 以及关系抽取 (Relation Extraction, RE) 任务的数据准备、模型训练、调优、评估、预测，各个阶段的详细说明。
-
-- [1. 数据准备](#1-数据准备)
- - [1.1. 准备数据集](#11-准备数据集)
- - [1.2. 自定义数据集](#12-自定义数据集)
- - [1.3. 数据下载](#13-数据下载)
-- [2. 开始训练](#2-开始训练)
- - [2.1. 启动训练](#21-启动训练)
- - [2.2. 断点训练](#22-断点训练)
- - [2.3. 混合精度训练](#24-混合精度训练)
- - [2.4. 分布式训练](#25-分布式训练)
- - [2.5. 知识蒸馏训练](#26-知识蒸馏训练)
- - [2.6. 其他训练环境](#27-其他训练环境)
-- [3. 模型评估与预测](#3-模型评估与预测)
- - [3.1. 指标评估](#31-指标评估)
- - [3.2. 测试信息抽取效果](#32-测试识别效果)
-- [4. 模型导出与预测](#4-模型导出与预测)
-- [5. FAQ](#5-faq)
-
-# 1. 数据准备
-
-## 1.1. 准备数据集
-
-在训练信息抽取相关模型时，PaddleOCR支持以下数据格式。
- - `通用数据` 用于训练以文本文件存储的数据集(SimpleDataSet);
-
-训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
-
-```
-# linux and mac os
-ln -sf /train_data/dataset
-# windows
-mklink /d /train_data/dataset
-```
-
-## 1.2. 自定义数据集
-
-训练过程中一般包含训练集与验证集，二者数据格式相同，下面介绍如何自定义数据集。
-
-**（1）训练集**
-
-建议将训练图片放入同一个文件夹，并用一个文本文件记录图片路径和标签，文本文件里的内容如下:
-
-```py
-" 图像文件名 图像标注信息 "
-zh_train_0.jpg [{"transcription": "汇丰晋信", "label": "other", "points": [[104, 114], [530, 114], [530, 175], [104, 175]], "id": 1, "linking": []}, {"transcription": "受理时间:", "label": "question", "points": [[126, 267], [266, 267], [266, 305], [126, 305]], "id": 7, "linking": [[7, 13]]}, {"transcription": "2020.6.15", "label": "answer", "points": [[321, 239], [537, 239], [537, 285], [321, 285]], "id": 13, "linking": [[7, 13]]}]
-zh_train_1.jpg [{"transcription": "中国人体器官捐献", "label": "other", "points": [[544, 459], [954, 459], [954, 517], [544, 517]], "id": 1, "linking": []}, {"transcription": ">编号:MC545715483585", "label": "other", "points": [[1462, 470], [2054, 470], [2054, 543], [1462, 543]], "id": 10, "linking": []}, {"transcription": "CHINAORGANDONATION", "label": "other", "points": [[543, 516], [958, 516], [958, 551], [543, 551]], "id": 14, "linking": []}, {"transcription": "中国人体器官捐献志愿登记表", "label": "header", "points": [[635, 793], [1892, 793], [1892, 904], [635, 904]], "id": 18, "linking": []}]
-...
-```
-
-**注意：** 文本文件中默认请将图片路径和图片标签用 `\t` 分割，如用其他方式分割将造成训练报错。
-
-其中图像标注信息字符串经过json解析之后可以得到一个列表信息，列表中每个元素是一个字典，存储了每个文本行的需要信息，各个字段的含义如下。
-
-- transcription: 存储了文本行的文字内容
-- label: 该文本行内容所属的类别
-- points: 存储文本行的四点位置信息
-- id: 存储文本行的id信息，用于RE任务的训练
-- linking: 存储文本行的之间的连接信息，用于RE任务的训练
-
-**（2）验证集**
-
-验证集构建方式与训练集相同。
-
-**（3）字典文件**
-
-训练集与验证集中的文本行包含标签信息，所有标签的列表存在字典文件中（如`class_list.txt`），字典文件中的每一行表示为一个类别名称。
-
-以XFUND_zh数据为例，共包含4个类别，字典文件内容如下所示。
-
-```
-OTHER
-QUESTION
-ANSWER
-HEADER
-```
-
-在标注文件中，每个标注的文本行内容的`label`字段标注信息需要属于字典内容。
-
-最终数据集应有如下文件结构：
-
-```
-|-train_data
- |-data_name
- |- train.json
- |- train
- |- zh_train_0.png
- |- zh_train_1.jpg
- | ...
- |- val.json
- |- val
- |- zh_val_0.png
- |- zh_val_1.jpg
- | ...
-```
-
-**注：**
-
-- 标注文件中的类别信息不区分大小写，如`HEADER`与`header`会被解析为相同的类别id，因此在标注的时候，不能使用小写处理后相同的字符串表示不同的类别。
-- 在整理标注文件的时候，建议将other这个类别（其他，无需关注的文本行可以标注为other）放在第一行，在解析的时候，会将`other`类别的类别id解析为0，后续不会对该类进行可视化。
-
-## 1.3. 数据下载
-
-如果你没有本地数据集，可以从[XFUND](https://github.com/doc-analysis/XFUND)或者[FUNSD](https://guillaumejaume.github.io/FUNSD/)官网下载数据，然后使用XFUND与FUNSD的处理脚本([XFUND](../../ppstructure/kie/tools/trans_xfun_data.py), [FUNSD](../../ppstructure/kie/tools/trans_funsd_label.py))，生成用于PaddleOCR训练的数据格式，并使用公开数据集快速体验关键信息抽取的流程。
-
-更多关于公开数据集的介绍，请参考[关键信息抽取数据集说明文档](./dataset/kie_datasets.md)。
-
-PaddleOCR也支持了关键信息抽取模型的标注，具体使用方法请参考：[PPOCRLabel使用文档](https://github.com/PFCCLab/PPOCRLabel/blob/main/README_ch.md)。
-
-
-# 2. 开始训练
-
-PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 VI-LayoutXLM 多模态预训练模型为例进行讲解。
-
-> 如果希望使用基于SDMGR的关键信息抽取算法，请参考：[SDMGR使用](./algorithm_kie_sdmgr.md)。
-
-## 2.1. 启动训练
-
-如果你没有使用自定义数据集，可以使用PaddleOCR中已经处理好的XFUND_zh数据集进行快速体验。
-
-```bash
-mkdir train_data
-cd train_data
-wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar && tar -xf XFUND.tar
-cd ..
-```
-
-如果不希望训练，直接体验后面的模型评估、预测、动转静、推理的流程，可以下载PaddleOCR中提供的预训练模型，并跳过2.1部分。
-
-使用下面的方法，下载基于XFUND数据的SER与RE任务预训练模型。
-
-```bash
-mkdir pretrained_model
-cd pretrained_model
-# 下载并解压SER预训练模型
-wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar & tar -xf ser_vi_layoutxlm_xfund_pretrained.tar
-
-# 下载并解压RE预训练模型
-wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar & tar -xf re_vi_layoutxlm_xfund_pretrained.tar
-```
-
-开始训练:
-
-- 如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false
-- PaddleOCR在训练时，会默认下载VI-LayoutXLM预训练模型，这里无需预先下载。
-
-```bash
-# GPU训练 支持单卡，多卡训练
-# 训练日志会自动保存到 配置文件中"{Global.save_model_dir}" 下的train.log文件中
-
-# SER单卡训练
-python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml
-
-# SER多卡训练，通过--gpus参数指定卡号
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml
-
-# RE任务单卡训练
-python3 tools/train.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml
-```
-
-以SER任务为例，正常启动训练后，会看到以下log输出：
-
-```
-[2022/08/08 16:28:28] ppocr INFO: epoch: [1/200], global_step: 10, lr: 0.000006, loss: 1.871535, avg_reader_cost: 0.28200 s, avg_batch_cost: 0.82318 s, avg_samples: 8.0, ips: 9.71838 samples/s, eta: 0:51:59
-[2022/08/08 16:28:33] ppocr INFO: epoch: [1/200], global_step: 19, lr: 0.000018, loss: 1.461939, avg_reader_cost: 0.00042 s, avg_batch_cost: 0.32037 s, avg_samples: 6.9, ips: 21.53773 samples/s, eta: 0:37:55
-[2022/08/08 16:28:39] ppocr INFO: cur metric, precision: 0.11526348939743859, recall: 0.19776657060518732, hmean: 0.14564265817747712, fps: 34.008392345050055
-[2022/08/08 16:28:45] ppocr INFO: save best model is to ./output/ser_vi_layoutxlm_xfund_zh/best_accuracy
-[2022/08/08 16:28:45] ppocr INFO: best metric, hmean: 0.14564265817747712, precision: 0.11526348939743859, recall: 0.19776657060518732, fps: 34.008392345050055, best_epoch: 1
-[2022/08/08 16:28:51] ppocr INFO: save model in ./output/ser_vi_layoutxlm_xfund_zh/latest
-```
-
-log 中自动打印如下信息：
-
-| 字段 | 含义 |
-| :----: | :------: |
-| epoch | 当前迭代轮次 |
-| iter | 当前迭代次数 |
-| lr | 当前学习率 |
-| loss | 当前损失函数 |
-| reader_cost | 当前 batch 数据处理耗时 |
-| batch_cost | 当前 batch 总耗时 |
-| samples | 当前 batch 内的样本数 |
-| ips | 每秒处理图片的数量 |
-
-
-PaddleOCR支持训练和评估交替进行, 可以在 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 中修改 `eval_batch_step` 设置评估频率，默认每19个iter评估一次。评估过程中默认将最佳hmean模型，保存为 `output/ser_vi_layoutxlm_xfund_zh/best_accuracy/` 。
-
-如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
-
-**提示：** 可通过 -c 参数选择 `configs/kie/` 路径下的多种模型配置进行训练，PaddleOCR支持的信息抽取算法可以参考[前沿算法列表](./algorithm_overview.md)。
-
-
-如果你希望训练自己的数据集，需要修改配置文件中的数据配置、字典文件以及类别数。
-
-
-以 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 为例，修改的内容如下所示。
-
-```yaml
-
-Architecture:
- # ...
- Backbone:
- name: LayoutXLMForSer
- pretrained: True
- mode: vi
- # 由于采用BIO标注，假设字典中包含n个字段（包含other）时，则类别数为2n-1; 假设字典中包含n个字段（不含other）时，则类别数为2n+1。否则在train过程会报：IndexError: (OutOfRange) label value should less than the shape of axis dimension 。
- num_classes: &num_classes 7
-
-PostProcess:
- name: kieSerTokenLayoutLMPostProcess
- # 修改字典文件的路径为你自定义的数据集的字典路径
- class_path: &class_path train_data/XFUND/class_list_xfun.txt
-
-Train:
- dataset:
- name: SimpleDataSet
- # 修改为你自己的训练数据目录
- data_dir: train_data/XFUND/zh_train/image
- # 修改为你自己的训练数据标签文件
- label_file_list:
- - train_data/XFUND/zh_train/train.json
- ...
- loader:
- # 训练时的单卡batch_size
- batch_size_per_card: 8
- ...
-
-Eval:
- dataset:
- name: SimpleDataSet
- # 修改为你自己的验证数据目录
- data_dir: train_data/XFUND/zh_val/image
- # 修改为你自己的验证数据标签文件
- label_file_list:
- - train_data/XFUND/zh_val/val.json
- ...
- loader:
- # 验证时的单卡batch_size
- batch_size_per_card: 8
-```
-
-**注意，预测/评估时的配置文件请务必与训练一致。**
-
-## 2.2. 断点训练
-
-如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定` Architecture.Backbone.checkpoints`指定要加载的模型路径：
-
-```bash
-python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy
-```
-
-**注意**：
-
-- `Architecture.Backbone.checkpoints`的优先级高于`Architecture.Backbone.pretrained`，需要加载之前训练好的训练模型进行模型微调、恢复训练、模型评估时，需要使用`Architecture.Backbone.checkpoints`指定模型参数路径；如果需要使用默认提供的通用预训练模型进行训练，则需要指定`Architecture.Backbone.pretrained`为`True`，同时指定`Architecture.Backbone.checkpoints`为空（`null`）。
-- LayoutXLM系列模型均是调用了PaddleNLP中的预训练模型，模型加载与保存的逻辑与PaddleNLP基本一致，因此在这里不需要指定`Global.pretrained_model`或者`Global.checkpoints`参数；此外，LayoutXLM系列模型的蒸馏训练目前不支持断点训练。
-
-
-## 2.3. 混合精度训练
-
-coming soon!
-
-## 2.4. 分布式训练
-
-多机多卡训练时，通过 `--ips` 参数设置使用的机器IP地址，通过 `--gpus` 参数设置使用的GPU ID：
-
-```bash
-python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml
-```
-
-**注意:** （1）采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通；（2）训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`；（3）更多关于分布式训练的性能优势等信息，请参考：[分布式训练教程](./distributed_training.md)。
-
-## 2.5. 知识蒸馏训练
-
-PaddleOCR支持了基于U-DML知识蒸馏的关键信息抽取模型训练过程，配置文件请参考：[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)，更多关于知识蒸馏的说明文档请参考：[知识蒸馏说明文档](./knowledge_distillation.md)。
-
-**注意**： PaddleOCR中LayoutXLM系列关键信息抽取模型的保存与加载逻辑与PaddleNLP保持一致，因此在蒸馏的过程中仅保存了学生模型的参数，如果希望使用保存的模型进行评估，需要使用学生模型的配置（上面的蒸馏文件对应的学生模型为[ser_vi_layoutxlm_xfund_zh.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml)）
-
-
-## 2.6. 其他训练环境
-
-- Windows GPU/CPU
-在Windows平台上与Linux平台略有不同:
-Windows平台只支持`单卡`的训练与预测，指定GPU进行训练`set CUDA_VISIBLE_DEVICES=0`
-在Windows平台，DataLoader只支持单进程模式，因此需要设置 `num_workers` 为0;
-
-- macOS
-不支持GPU模式，需要在配置文件中设置`use_gpu`为False，其余训练评估预测命令与Linux GPU完全相同。
-
-- Linux DCU
-DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`，其余训练评估预测命令与Linux GPU完全相同。
-
-
-# 3. 模型评估与预测
-
-## 3.1. 指标评估
-
-训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Architecture.Backbone.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 修改Eval中的 `label_file_path` 设置。
-
-```bash
-# GPU 评估， Global.checkpoints 为待测权重
-python3 tools/eval.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy
-```
-
-会输出以下信息，打印出precision、recall、hmean等信息。
-
-```py
-[2022/08/09 07:59:28] ppocr INFO: metric eval ***************
-[2022/08/09 07:59:28] ppocr INFO: precision:0.697476609016161
-[2022/08/09 07:59:28] ppocr INFO: recall:0.8861671469740634
-[2022/08/09 07:59:28] ppocr INFO: hmean:0.7805806758686339
-[2022/08/09 07:59:28] ppocr INFO: fps:17.367364606899105
-```
-
-
-## 3.2. 测试信息抽取结果
-
-使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
-
-默认预测的图片存储在 `infer_img` 里，通过 `-o Architecture.Backbone.checkpoints` 加载训练好的参数文件：
-
-根据配置文件中设置的 `save_model_dir` 和 `save_epoch_step` 字段，会有以下几种参数被保存下来：
-
-```
-output/ser_vi_layoutxlm_xfund_zh/
-├── best_accuracy
- ├── metric.states
- ├── model_config.json
- ├── model_state.pdparams
-├── best_accuracy.pdopt
-├── config.yml
-├── train.log
-├── latest
- ├── metric.states
- ├── model_config.json
- ├── model_state.pdparams
-├── latest.pdopt
-```
-
-其中 best_accuracy.* 是评估集上的最优模型；latest.* 是最新保存的一个模型。
-
-
-预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` 完成了模型的训练过程。
-
-您可以使用如下命令进行中文模型预测。
-
-
-```bash
-python3 tools/infer_kie_token_ser.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.infer_img=./ppstructure/docs/kie/input/zh_val_42.jpg
-```
-
-预测图片如下所示，图片会存储在`Global.save_res_path`路径中。
-
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-PP-OCRv3 英文模型
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-PP-OCRv3 多语言模型
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-
-
-## 5. 使用教程
-
-
-### 5.1 快速体验
-
-- 在线网站体验：超轻量PP-OCR mobile模型体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
-- 移动端demo体验：[安装包DEMO下载地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)
-- 一行命令快速使用：[快速开始（中英文/多语言）](./doc/doc_ch/quickstart.md)
-
-
-### 5.2 模型训练、压缩、推理部署
-
-更多教程，包括模型训练、模型压缩、推理部署等，请参考[文档教程](../../README.md#文档教程)。
-
-
-## 6. 模型库
-
-PP-OCR中英文模型列表如下：
-
-| 模型简介 | 模型名称 | 推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
-| ------------------------------------- | ----------------------- | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| 中英文超轻量 PP-OCRv4 模型（15.8M） | ch_PP-OCRv4_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_train.tar) |
-| 中英文超轻量PP-OCRv3模型（16.2M） | ch_PP-OCRv3_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
-| 英文超轻量PP-OCRv3模型（13.4M） | en_PP-OCRv3_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
-| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
-| 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
-| 中英文通用PP-OCR server模型（143.4M） | ch_ppocr_server_v2.0_xx | 服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
-
-更多模型下载（包括英文数字模型、多语言模型、Paddle-Lite模型等），可以参考[PP-OCR 系列模型下载](./models_list.md)。
diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md
deleted file mode 100644
index 65b29b1879..0000000000
--- a/doc/doc_ch/quickstart.md
+++ /dev/null
@@ -1,297 +0,0 @@
-# PaddleOCR 快速开始
-
-**说明：** 本文主要介绍PaddleOCR wheel包对PP-OCR系列模型的快速使用，如要体验文档分析相关功能，请参考[PP-Structure快速使用教程](../../ppstructure/docs/quickstart.md)。
-
-- [1. 安装](#1)
- - [1.1 安装PaddlePaddle](#11)
- - [1.2 安装PaddleOCR whl包](#12)
-- [2. 便捷使用](#2)
- - [2.1 命令行使用](#21)
- - [2.1.1 中英文模型](#211)
- - [2.1.2 多语言模型](#212)
- - [2.2 Python脚本使用](#22)
- - [2.2.1 中英文与多语言使用](#221)
-- [3.小结](#3)
-
-
-
-## 1. 安装
-
-
-### 1.1 安装PaddlePaddle
-
-> 如果您没有基础的Python运行环境，请参考[运行环境准备](./environment.md)。
-
-- 您的机器安装的是CUDA 11，请运行以下命令安装
-
- ```bash
- pip install paddlepaddle-gpu
- ```
-
-- 您的机器是CPU，请运行以下命令安装
-
- ```bash
- pip install paddlepaddle
- ```
-
-更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
-
-
-### 1.2 安装PaddleOCR whl包
-
-```bash
-pip install paddleocr
-```
-
-
-## 2. 便捷使用
-
-### 2.1 命令行使用
-
-PaddleOCR提供了一系列测试图片，点击[这里](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip)下载并解压，然后在终端中切换到相应目录
-
-```
-cd /path/to/ppocr_img
-```
-
-如果不使用提供的测试图片，可以将下方`--image_dir`参数替换为相应的测试图片路径。
-
-
-#### 2.1.1 中英文模型
-
-* 检测+方向分类器+识别全流程：`--use_angle_cls true`设置使用方向分类器识别180度旋转文字，`--use_gpu false`设置不使用GPU
-
- ```bash
- paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true --use_gpu false
- ```
-
- 结果是一个list，每个item包含了文本框，文字和识别置信度
-
- ```bash
- [[[28.0, 37.0], [302.0, 39.0], [302.0, 72.0], [27.0, 70.0]], ('纯臻营养护发素', 0.9658738374710083)]
- ......
- ```
-
- 此外，paddleocr也支持输入pdf文件，并且可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
- ```bash
- paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
- ```
-
-- 单独使用检测：设置`--rec`为`false`
-
- ```bash
- paddleocr --image_dir ./imgs/11.jpg --rec false
- ```
-
- 结果是一个list，每个item只包含文本框
-
- ```bash
- [[27.0, 459.0], [136.0, 459.0], [136.0, 479.0], [27.0, 479.0]]
- [[28.0, 429.0], [372.0, 429.0], [372.0, 445.0], [28.0, 445.0]]
- ......
- ```
-
-- 单独使用识别：设置`--det`为`false`
-
- ```bash
- paddleocr --image_dir ./imgs_words/ch/word_1.jpg --det false
- ```
-
- 结果是一个list，每个item只包含识别结果和识别置信度
-
- ```bash
- ['韩国小馆', 0.994467]
- ```
-
-**版本说明**
-paddleocr默认使用PP-OCRv4模型(`--ocr_version PP-OCRv4`)，如需使用其他版本可通过设置参数`--ocr_version`，具体版本说明如下：
-| 版本名称 | 版本说明 |
-| --- | --- |
-| PP-OCRv4 | 支持中、英文检测和识别，方向分类器，支持多语种识别 |
-| PP-OCRv3 | 支持中、英文检测和识别，方向分类器，支持多语种识别 |
-| PP-OCRv2 | 支持中英文的检测和识别，方向分类器，多语言暂未更新 |
-| PP-OCR | 支持中、英文检测和识别，方向分类器，支持多语种识别 |
-
-如需新增自己训练的模型，可以在[paddleocr](../../paddleocr.py)中增加模型链接和字段，重新编译即可。
-
-更多whl包使用可参考[whl包文档](./whl.md)
-
-
-
-#### 2.1.2 多语言模型
-
-PaddleOCR目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`。
-
-``` bash
-paddleocr --image_dir ./imgs_en/254.jpg --lang=en
-```
-
-
-
-
-
-
-
', '', ...]}, # 表格的HTML字符串
- 'cells': [
- {
- 'tokens': ['P', 'a', 'd', 'd', 'l', 'e', 'P', 'a', 'd', 'd', 'l', 'e'], # 表格中的单个文本
- 'bbox': [x0, y0, x1, y1] # 表格中的单个文本的坐标
- }
- ]
- }
-}
-```
-
-训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
-
-```
-# linux and mac os
-ln -sf /train_data/dataset
-# windows
-mklink /d /train_data/dataset
-```
-
-## 1.2. 数据下载
-
-公开数据集下载可参考 [table_datasets](dataset/table_datasets.md)。
-
-## 1.3. 数据集生成
-
-使用[TableGeneration](https://github.com/WenmuZhou/TableGeneration)可进行扫描表格图像的生成。
-
-TableGeneration是一个开源表格数据集生成工具，其通过浏览器渲染的方式对html字符串进行渲染后获得表格图像。部分样张如下：
-
-|类型|样例|
-|---|---|
-|简单表格||
-|彩色表格||
-
-## 1.4 数据标注
-
-数据标注可参考[PPOCRLabel](https://github.com/PFCCLab/PPOCRLabel/blob/main/README_ch.md)
-
-# 2. 开始训练
-
-PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 [SLANet](../../configs/table/SLANet.yml) 模型训练PubTabNet英文数据集为例：
-
-## 2.1. 启动训练
-
-*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
-
-```
-# GPU训练 支持单卡，多卡训练
-# 训练日志会自动保存为 "{save_model_dir}" 下的train.log
-
-#单卡训练（训练周期长，不建议）
-python3 tools/train.py -c configs/table/SLANet.yml
-
-#多卡训练，通过--gpus参数指定卡号
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/SLANet.yml
-```
-
-正常启动训练后，会看到以下log输出：
-
-```
-[2022/08/16 03:07:33] ppocr INFO: epoch: [1/400], global_step: 20, lr: 0.000100, acc: 0.000000, loss: 3.915012, structure_loss: 3.229450, loc_loss: 0.670590, avg_reader_cost: 2.63382 s, avg_batch_cost: 6.32390 s, avg_samples: 48.0, ips: 7.59025 samples/s, eta: 9 days, 2:29:27
-[2022/08/16 03:08:41] ppocr INFO: epoch: [1/400], global_step: 40, lr: 0.000100, acc: 0.000000, loss: 1.750859, structure_loss: 1.082116, loc_loss: 0.652822, avg_reader_cost: 0.02533 s, avg_batch_cost: 3.37251 s, avg_samples: 48.0, ips: 14.23271 samples/s, eta: 6 days, 23:28:43
-[2022/08/16 03:09:46] ppocr INFO: epoch: [1/400], global_step: 60, lr: 0.000100, acc: 0.000000, loss: 1.395154, structure_loss: 0.776803, loc_loss: 0.625030, avg_reader_cost: 0.02550 s, avg_batch_cost: 3.26261 s, avg_samples: 48.0, ips: 14.71214 samples/s, eta: 6 days, 5:11:48
-```
-
-log 中自动打印如下信息：
-
-| 字段 | 含义 |
-| :----: | :------: |
-| epoch | 当前迭代轮次 |
-| global_step | 当前迭代次数 |
-| lr | 当前学习率 |
-| acc | 当前batch的准确率 |
-| loss | 当前损失函数 |
-| structure_loss | 表格结构损失值 |
-| loc_loss | 单元格坐标损失值 |
-| avg_reader_cost | 当前 batch 数据处理耗时 |
-| avg_batch_cost | 当前 batch 总耗时 |
-| avg_samples | 当前 batch 内的样本数 |
-| ips | 每秒处理图片的数量 |
-
-
-PaddleOCR支持训练和评估交替进行, 可以在 `configs/table/SLANet.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/SLANet/best_accuracy` 。
-
-如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
-
-**提示：** 可通过 -c 参数选择 `configs/table/` 路径下的多种模型配置进行训练，PaddleOCR支持的表格识别算法可以参考[前沿算法列表](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)：
-
-**注意，预测/评估时的配置文件请务必与训练一致。**
-
-## 2.2. 断点训练
-
-如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
-```shell
-python3 tools/train.py -c configs/table/SLANet.yml -o Global.checkpoints=./your/trained/model
-```
-
-**注意**：`Global.checkpoints`的优先级高于`Global.pretrained_model`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrained_model`指定的模型。
-
-## 2.3. 更换Backbone 训练
-
-PaddleOCR将网络划分为四部分，分别在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones->necks->heads)依次通过这四个部分。
-
-```bash
-├── architectures # 网络的组网代码
-├── transforms # 网络的图像变换模块
-├── backbones # 网络的特征提取模块
-├── necks # 网络的特征增强模块
-└── heads # 网络的输出模块
-```
-如果要更换的Backbone 在PaddleOCR中有对应实现，直接修改配置yml文件中`Backbone`部分的参数即可。
-
-如果要使用新的Backbone，更换backbones的例子如下:
-
-1. 在 [ppocr/modeling/backbones](../../ppocr/modeling/backbones) 文件夹下新建文件，如my_backbone.py。
-2. 在 my_backbone.py 文件内添加相关代码，示例代码如下:
-
-```python
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-
-class MyBackbone(nn.Layer):
- def __init__(self, *args, **kwargs):
- super(MyBackbone, self).__init__()
- # your init code
- self.conv = nn.xxxx
-
- def forward(self, inputs):
- # your network forward
- y = self.conv(inputs)
- return y
-```
-
-3. 在 [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py)文件内导入添加的`MyBackbone`模块，然后修改配置文件中Backbone进行配置即可使用，格式如下:
-
-```yaml
-Backbone:
-name: MyBackbone
-args1: args1
-```
-
-**注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
-
-## 2.4. 混合精度训练
-
-如果您想进一步加快训练速度，可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html)， 以单机单卡为例，命令如下：
-
-```shell
-python3 tools/train.py -c configs/table/SLANet.yml \
- -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy \
- Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
- ```
-
-## 2.5. 分布式训练
-
-多机多卡训练时，通过 `--ips` 参数设置使用的机器IP地址，通过 `--gpus` 参数设置使用的GPU ID：
-
-```bash
-python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/table/SLANet.yml \
- -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy
-```
-
-**注意:** （1）采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通；（2）训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`；（3）更多关于分布式训练的性能优势等信息，请参考：[分布式训练教程](./distributed_training.md)。
-
-
-## 2.6. 其他训练环境
-
-- Windows GPU/CPU
-在Windows平台上与Linux平台略有不同:
-Windows平台只支持`单卡`的训练与预测，指定GPU进行训练`set CUDA_VISIBLE_DEVICES=0`
-在Windows平台，DataLoader只支持单进程模式，因此需要设置 `num_workers` 为0;
-
-- macOS
-不支持GPU模式，需要在配置文件中设置`use_gpu`为False，其余训练评估预测命令与Linux GPU完全相同。
-
-- Linux DCU
-DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`，其余训练评估预测命令与Linux GPU完全相同。
-
-## 2.7. 模型微调
-
-### 2.7.1 数据选择
-
-数据量：建议至少准备2000张的表格识别数据集用于模型微调。
-
-### 2.7.2 模型选择
-
-建议选择SLANet模型（配置文件：[SLANet_ch.yml](../../configs/table/SLANet_ch.yml)，预训练模型：[ch_ppstructure_mobile_v2.0_SLANet_train.tar](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar)）进行微调，其精度与泛化性能是目前提供的最优中文表格预训练模型。
-
-更多表格识别模型，请参考[PP-Structure 系列模型库](../../ppstructure/docs/models_list.md)。
-
-### 2.7.3 训练超参选择
-
-在模型微调的时候，最重要的超参就是预训练模型路径`pretrained_model`, 学习率`learning_rate`，部分配置文件如下所示。
-
-```yaml
-Global:
- pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # 预训练模型路径
-Optimizer:
- lr:
- name: Cosine
- learning_rate: 0.001 #
- warmup_epoch: 0
- regularizer:
- name: 'L2'
- factor: 0
-```
-
-上述配置文件中，首先需要将`pretrained_model`字段指定为`best_accuracy.pdparams`文件路径。
-
-PaddleOCR提供的配置文件是在4卡训练（相当于总的batch size是`4*48=192`）、且没有加载预训练模型情况下的配置文件，因此您的场景中，学习率与总的batch size需要对应线性调整，例如
-
-* 如果您的场景中是单卡训练，单卡batch_size=48，则总的batch_size=48，建议将学习率调整为`0.00025`左右。
-* 如果您的场景中是单卡训练，由于显存限制，只能设置单卡batch_size=32，则总的batch_size=32，建议将学习率调整为`0.00017`左右。
-
-
-# 3. 模型评估与预测
-
-## 3.1. 指标评估
-
-训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/table/SLANet.yml` 修改Eval中的 `label_file_list` 设置。
-
-
-```
-# GPU 评估， Global.checkpoints 为待测权重
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/table/SLANet.yml -o Global.checkpoints={path/to/weights}/best_accuracy
-```
-
-运行完成后，会输出模型的acc指标，如对英文表格识别模型进行评估，会见到如下输出。
-```bash
-[2022/08/16 07:59:55] ppocr INFO: acc:0.7622245132160782
-[2022/08/16 07:59:55] ppocr INFO: fps:30.991640622573044
-```
-
-## 3.2. 测试表格结构识别效果
-
-使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
-
-默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 加载训练好的参数文件：
-
-根据配置文件中设置的 `save_model_dir` 和 `save_epoch_step` 字段，会有以下几种参数被保存下来：
-
-```
-output/SLANet/
-├── best_accuracy.pdopt
-├── best_accuracy.pdparams
-├── best_accuracy.states
-├── config.yml
-├── latest.pdopt
-├── latest.pdparams
-├── latest.states
-└── train.log
-```
-其中 best_accuracy.* 是评估集上的最优模型；latest.* 是最后一个epoch的模型。
-
-```
-# 预测表格图像
-python3 tools/infer_table.py -c configs/table/SLANet.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=ppstructure/docs/table/table.jpg
-```
-
-预测图片：
-
-
-
-得到输入图像的预测结果：
-
-```
-['', '', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' ', '', ''],[[320.0562438964844, 197.83375549316406, 350.0928955078125, 214.4309539794922], ... , [318.959228515625, 271.0166931152344, 353.7394104003906, 286.4538269042969]]
-```
-
-单元格坐标可视化结果为
-
-
-
-# 4. 模型导出与预测
-
-## 4.1 模型导出
-
-inference 模型（`paddle.jit.save`保存的模型）
-一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。
-训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，多用于恢复训练等。
-与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合于实际系统集成。
-
-表格识别模型转inference模型与文字检测识别的方式相同，如下：
-
-```
-# -c 后面设置训练算法的yml配置文件
-# -o 配置可选参数
-# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
-# Global.save_inference_dir参数设置转换的模型将保存的地址。
-
-python3 tools/export_model.py -c configs/table/SLANet.yml -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy Global.save_inference_dir=./inference/SLANet/
-```
-
-转换成功后，在目录下有三个文件：
-
-```
-inference/SLANet/
- ├── inference.pdiparams # inference模型的参数文件
- ├── inference.pdiparams.info # inference模型的参数信息，可忽略
- └── inference.pdmodel # inference模型的program文件
-```
-
-## 4.2 模型预测
-
-模型导出后，使用如下命令即可完成inference模型的预测
-
-```python
-python3.7 table/predict_structure.py \
- --table_model_dir={path/to/inference model} \
- --table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
- --image_dir=docs/table/table.jpg \
- --output=../output/table
-```
-
-预测图片：
-
-
-
-得到输入图像的预测结果：
-
-```
-['', '', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' ', '', ''],[[320.0562438964844, 197.83375549316406, 350.0928955078125, 214.4309539794922], ... , [318.959228515625, 271.0166931152344, 353.7394104003906, 286.4538269042969]]
-```
-
-单元格坐标可视化结果为
-
-
-
-
-# 5. FAQ
-
-Q1: 训练模型转inference 模型之后预测效果不一致？
-
-**A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。可以对比训练使用的配置文件中的预处理、后处理和预测时是否存在差异。
diff --git a/doc/doc_ch/thirdparty.md b/doc/doc_ch/thirdparty.md
deleted file mode 100644
index ff9059cdf6..0000000000
--- a/doc/doc_ch/thirdparty.md
+++ /dev/null
@@ -1,143 +0,0 @@
-# 社区贡献
-
-感谢大家长久以来对PaddleOCR的支持和关注，与广大开发者共同构建一个专业、和谐、相互帮助的开源社区是PaddleOCR的目标。本文档展示了已有的社区贡献、对于各类贡献说明、新的机会与流程，希望贡献流程更加高效、路径更加清晰。
-
-PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实现自己的想法，享受创造价值带来的愉悦。
-
----
-
-
- 
-
-
-> 上图为PaddleOCR目前的Contributor，定期更新
-
-## 1. 社区贡献
-
-### 1.1 基于PaddleOCR的社区项目
-
-| 类别 | 项目 | 描述 | 开发者 |
-| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------------------------------------ |
-| 通用工具 | [FastOCRLabel](https://gitee.com/BaoJianQiang/FastOCRLabel) | 完整的C#版本标注GUI | [包建强](https://gitee.com/BaoJianQiang) |
-| 通用工具 | [DangoOCR离线版](https://github.com/PantsuDango/DangoOCR) | 通用型桌面级即时翻译GUI | [PantsuDango](https://github.com/PantsuDango) |
-| 通用工具 | [scr2txt](https://github.com/lstwzd/scr2txt) | 截屏转文字GUI | [lstwzd](https://github.com/lstwzd) |
-| 通用工具 | [ocr_sdk](https://github.com/mymagicpower/AIAS/blob/main/1_image_sdks/text_recognition/ocr_sdk) | OCR java SDK工具箱 | [Calvin](https://github.com/mymagicpower) |
-| 通用工具 | [iocr](https://github.com/mymagicpower/AIAS/blob/main/8_suite_hub/iocr) | IOCR 自定义模板识别(支持表格识别) | [Calvin](https://github.com/mymagicpower) |
-| 通用工具 | [Lmdb Dataset Format Conversion Tool](https://github.com/OneYearIsEnough/PaddleOCR-Recog-LmdbDataset-Conversion) | 文本识别任务中lmdb数据格式转换工具 | [OneYearIsEnough](https://github.com/OneYearIsEnough) |
-| 通用工具 | [用paddleocr打造一款“盗幕笔记”](https://github.com/kjf4096/paddleocr_dmbj) | 用PaddleOCR记笔记 | [kjf4096](https://github.com/kjf4096) |
-| 垂类工具 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/1054614?channelType=0&channel=0) | 英文视频自动生成字幕 | [叶月水狐](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/322052) |
-| 垂类工具 | [id_card_ocr](https://github.com/baseli/id_card_ocr) | 身份证复印件识别 | [baseli](https://github.com/baseli) |
-| 垂类工具 | [Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader) | 能看懂表格图片的数据助手 | [thunder95](https://github.com/thunder95]) |
-| 垂类工具 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3382897) | OCR流程中对手写体进行过滤 | [daassh](https://github.com/daassh) |
-| 垂类场景调优 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/2803693) | 电表读数和编号识别 | [深渊上的坑](https://github.com/edencfc) |
-| 垂类场景调优 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3284199) | LCD液晶字符检测 | [Dream拒杰](https://github.com/zhangyingying520) |
-| 前后处理 | [paddleOCRCorrectOutputs](https://github.com/yuranusduke/paddleOCRCorrectOutputs) | 获取OCR识别结果的key-value | [yuranusduke](https://github.com/yuranusduke) |
-|前处理| [optlab](https://github.com/GreatV/optlab) |OCR前处理工具箱，基于Qt和Leptonica。|[GreatV](https://github.com/GreatV)|
-|应用部署| [PaddleOCRSharp](https://github.com/raoyutian/PaddleOCRSharp) |PaddleOCR的.NET封装与应用部署。|[raoyutian](https://github.com/raoyutian/PaddleOCRSharp)|
-|应用部署| [PaddleSharp](https://github.com/sdcb/PaddleSharp) |PaddleOCR的.NET封装与应用部署，支持跨平台、GPU|[sdcb](https://github.com/sdcb)|
-| 应用部署 | [PaddleOCR-Streamlit-Demo](https://github.com/Lovely-Pig/PaddleOCR-Streamlit-Demo) | 使用Streamlit部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) |
-| 应用部署 | [PaddleOCR-PyWebIO-Demo](https://github.com/Lovely-Pig/PaddleOCR-PyWebIO-Demo) | 使用PyWebIO部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) |
-| 应用部署 | [PaddleOCR-Paddlejs-Vue-Demo](https://github.com/Lovely-Pig/PaddleOCR-Paddlejs-Vue-Demo) | 使用Paddle.js和Vue部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) |
-| 应用部署 | [PaddleOCR-Paddlejs-React-Demo](https://github.com/Lovely-Pig/PaddleOCR-Paddlejs-React-Demo) | 使用Paddle.js和React部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) |
-| 学术前沿模型训练与推理 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3397137) | StarNet-MobileNetV3算法–中文训练 | [xiaoyangyang2](https://github.com/xiaoyangyang2) |
-| 学术前沿模型训练与推理 | [ABINet-paddle](https://github.com/Huntersdeng/abinet-paddle) | ABINet算法前向运算的paddle实现以及模型各部分的实现细节分析 | [Huntersdeng](https://github.com/Huntersdeng) |
-
-### 1.2 为PaddleOCR新增功能
-
-- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android([#340](https://github.com/PaddlePaddle/PaddleOCR/pull/340))和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码([#325](https://github.com/PaddlePaddle/PaddleOCR/pull/325))
-- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务([#507](https://github.com/PaddlePaddle/PaddleOCR/pull/507))。
-- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用([#1027](https://github.com/PaddlePaddle/PaddleOCR/pull/1027))。
-- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了[PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/README_ch.md) 的完整代码。
-- 非常感谢 [bupt906](https://github.com/bupt906) 贡献MicroNet结构代码([#5251](https://github.com/PaddlePaddle/PaddleOCR/pull/5251))和贡献OneCycle学习率策略代码([#5252](https://github.com/PaddlePaddle/PaddleOCR/pull/5252))
-
-### 1.3 代码修复
-
-- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题([#210](https://github.com/PaddlePaddle/PaddleOCR/pull/210))。
-- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码([#304](https://github.com/PaddlePaddle/PaddleOCR/pull/304))。
-- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格([so many commits)](https://github.com/PaddlePaddle/PaddleOCR/commits?author=BeyondYourself)。
-
-### 1.4 文档优化与翻译
-
-- 非常感谢 **[RangeKing](https://github.com/RangeKing)，[HustBestCat](https://github.com/HustBestCat)，[v3fc](https://github.com/v3fc)，[1084667371](https://github.com/1084667371)** 贡献翻译《动手学OCR》notebook[电子书英文版](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/notebook/notebook_en)。
-- 非常感谢 [thunderstudying](https://github.com/thunderstudying)，[RangeKing](https://github.com/RangeKing)，[livingbody](https://github.com/livingbody)， [WZMIAOMIAO](https://github.com/WZMIAOMIAO)，[haigang1975](https://github.com/haigang1975) 补充多个英文markdown文档。
-- 非常感谢 **[fanruinet](https://github.com/fanruinet)** 润色和修复35篇英文文档([#5205](https://github.com/PaddlePaddle/PaddleOCR/pull/5205))。
-- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档。
-
-### 1.5 多语言语料
-
-- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集([#321](https://github.com/PaddlePaddle/PaddleOCR/pull/321))。
-- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料([#954](https://github.com/PaddlePaddle/PaddleOCR/pull/954))。
-
-## 2. 贡献说明
-
-### 2.1 新增功能类
-
-PaddleOCR非常欢迎社区贡献以PaddleOCR为核心的各种服务、部署实例与软件应用，经过认证的社区贡献会被添加在上述社区贡献表中，为广大开发者增加曝光，也是PaddleOCR的荣耀，其中：
-
-- 项目形式：官方社区认证的项目代码应有良好的规范和结构，同时，还应配备一个详细的README.md，说明项目的使用方法。通过在requirements.txt文件中增加一行 `paddleocr` 可以自动收录到PaddleOCR的usedby中。
-
-- 合入方式：如果是对PaddleOCR现有工具的更新升级，则会合入主repo。如果为PaddleOCR拓展了新功能，请先与官方人员联系，确认项目是否合入主repo，*即使新功能未合入主repo，我们同样也会以社区贡献的方式为您的个人项目增加曝光。*
-
-
-### 2.2 代码优化
-
-如果您在使用PaddleOCR时遇到了代码bug、功能不符合预期等问题，可以为PaddleOCR贡献您的修改，其中：
-
-- Python代码规范可参考[附录1：Python代码规范](./code_and_doc.md/#附录1)。
-
-- 提交代码前请再三确认不会引入新的bug，并在PR中描述优化点。如果该PR解决了某个issue，请在PR中连接到该issue。所有的PR都应该遵守附录3中的[3.2.10 提交代码的一些约定。](./code_and_doc.md/#提交代码的一些约定)
-
-- 请在提交之前参考下方的[附录3：Pull Request说明](./code_and_doc.md#附录3)。如果您对git的提交流程不熟悉，同样可以参考附录3的3.2节。
-
-**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理**。
-
-### 2.3 文档优化
-
-如果您在使用PaddleOCR时遇到了文档表述不清楚、描述缺失、链接失效等问题，可以为PaddleOCR贡献您的修改。文档书写规范请参考[附录2：文档规范](./code_and_doc.md/#附录2)。**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
-
-## 3. 更多贡献机会
-
-我们非常鼓励开发者使用PaddleOCR实现自己的想法，同时我们也列出一些经过分析后认为有价值的拓展方向，整体收集在社区项目常规赛中。
-
-## 4. 联系我们
-
-我们非常欢迎广大开发者在有意向为PaddleOCR贡献代码、文档、语料等内容前与我们联系，这样可以大大降低PR过程中的沟通成本。同时，如果您觉得某些想法个人难以实现，我们也可以通过SIG的形式定向为项目招募志同道合的开发者一起共建。通过SIG渠道贡献的项目将会获得深层次的研发支持与运营资源（如公众号宣传、直播课等）。
-
-我们推荐的贡献流程是：
-
-- 通过在github issue的题目中增加 `【third-party】` 标记，说明遇到的问题（以及解决的思路）或想拓展的功能，等待值班人员回复。例如 `【third-party】为PaddleOCR贡献IOS示例`
-- 与我们沟通确认技术方案或bug、优化点准确无误后进行功能新增或相应的修改，代码与文档遵循相关规范。
-- PR链接到上述issue，等待review。
-
-## 5. 致谢与后续
-
- - 合入代码之后会在本文档第一节中更新信息，默认链接为github名字及主页，如果有需要更换主页，也可以联系我们。
- - 新增重要功能类，会在用户群广而告之，享受开源社区荣誉时刻。
- - **如果您有基于PaddleOCR的项目，但未出现在上述列表中，请按照 `4. 联系我们` 的步骤与我们联系。**
-
-
-
-## 附录：社区常规赛积分榜
-
-| 开发者 | 总积分 | 开发者 | 总积分 |
-| ------------------------------------------------------- | ------ | ----------------------------------------------------- | ------ |
-| [RangeKing](https://github.com/RangeKing) | 220 | [WZMIAOMIAO](https://github.com/WZMIAOMIAO) | 36 |
-| [hao6699](https://github.com/hao6699) | 145 | [v3fc](https://github.com/v3fc) | 35 |
-| [mymagicpower](https://github.com/mymagicpower) | 140 | [imiyu](https://github.com/imiyu) | 30 |
-| [raoyutian](https://github.com/raoyutian) | 90 | [haigang1975](https://github.com/haigang1975) | 29 |
-| [sdcb](https://github.com/sdcb) | 80 | [daassh](https://github.com/daassh) | 23 |
-| [zhiminzhang0830](https://github.com/zhiminzhang0830) | 70 | [xiaoyangyang2](https://github.com/xiaoyangyang2) | 20 |
-| [Lovely-Pig](https://github.com/Lovely-Pig) | 70 | [prettyocean85](https://github.com/prettyocean85) | 20 |
-| [livingbody](https://github.com/livingbody) | 70 | [nmusik](https://github.com/nmusik) | 20 |
-| [fanruinet](https://github.com/fanruinet) | 70 | [kjf4096](https://github.com/kjf4096) | 20 |
-| [bupt906](https://github.com/bupt906) | 60 | [chccc1994](https://github.com/chccc1994) | 20 |
-| [edencfc](https://github.com/edencfc) | 57 | [BeyondYourself ](https://github.com/BeyondYourself) | 20 |
-| [zhangyingying520](https://github.com/zhangyingying520) | 57 | chenguoqi08161 | 18 |
-| [ITerydh](https://github.com/ITerydh) | 55 | [weiwenlan](https://github.com/weiwenlan) | 10 |
-| [telppa](https://github.com/telppa) | 40 | [shaoshenchen thinc](https://github.com/shaoshenchen) | 10 |
-| sosojust1984 | 40 | [jordan2013](https://github.com/jordan2013) | 10 |
-| [redearly123](https://github.com/redearly123) | 40 | [JimEverest](https://github.com/JimEverest) | 10 |
-| [OneYearIsEnough](https://github.com/OneYearIsEnough) | 40 | [HustBestCat](https://github.com/HustBestCat) | 10 |
-| [Huntersdeng](https://github.com/Huntersdeng) | 40 | | |
-| [GreatV](https://github.com/GreatV) | 40 | | |
-| CLXK294 | 40 | | |
diff --git a/doc/doc_ch/training.md b/doc/doc_ch/training.md
deleted file mode 100644
index 37c2ec26ed..0000000000
--- a/doc/doc_ch/training.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# PP-OCR模型训练
-
-本文将介绍模型训练时需掌握的基本概念，和训练时的调优方法。
-
-同时会简单介绍PaddleOCR模型训练数据的组成部分，以及如何在垂类场景中准备数据finetune模型。
-
-- [1. 配置文件说明](#1-配置文件说明)
-- [2. 基本概念](#2-基本概念)
- - [2.1 学习率](#21-学习率)
- - [2.2 正则化](#22-正则化)
- - [2.3 评估指标](#23-评估指标)
-- [3. 数据与垂类场景](#3-数据与垂类场景)
- - [3.1 训练数据](#31-训练数据)
- - [3.2 垂类场景](#32-垂类场景)
- - [3.3 自己构建数据集](#33-自己构建数据集)
-- [4. 常见问题](#4-常见问题)
-
-
-
-## 1. 配置文件说明
-
-PaddleOCR模型使用配置文件管理网络训练、评估的参数。在配置文件中，可以设置组建模型、优化器、损失函数、模型前后处理的参数，PaddleOCR从配置文件中读取到这些参数，进而组建出完整的训练流程，完成模型训练，在需要对模型进行优化的时，可以通过修改配置文件中的参数完成配置，使用简单且方便修改。
-
-完整的配置文件说明可以参考[配置文件](./config.md)
-
-
-
-## 2. 基本概念
-
-模型训练过程中需要手动调整一些超参数，帮助模型以最小的代价获得最优指标。不同的数据量可能需要不同的超参，当您希望在自己的数据上finetune或对模型效果调优时，有以下几个参数调整策略可供参考：
-
-
-### 2.1 学习率
-
-学习率是训练神经网络的重要超参数之一，它代表在每一次迭代中梯度向损失函数最优解移动的步长。
-在PaddleOCR中提供了多种学习率更新策略,可以通过配置文件修改，例如：
-
-```
-Optimizer:
- ...
- lr:
- name: Piecewise
- decay_epochs : [700, 800]
- values : [0.001, 0.0001]
- warmup_epoch: 5
-```
-
-Piecewise 代表分段常数衰减，在不同的学习阶段指定不同的学习率，在每段内学习率相同。
-warmup_epoch 代表在前5个epoch中，学习率将逐渐从0增加到base_lr。全部策略可以参考代码[learning_rate.py](../../ppocr/optimizer/learning_rate.py) 。
-
-
-### 2.2 正则化
-
-正则化可以有效的避免算法过拟合，PaddleOCR中提供了L1、L2正则方法，L1 和 L2 正则化是最常用的正则化方法。L1 正则化向目标函数添加正则化项，以减少参数的绝对值总和；而 L2 正则化中，添加正则化项的目的在于减少参数平方的总和。配置方法如下：
-
-```
-Optimizer:
- ...
- regularizer:
- name: L2
- factor: 2.0e-05
-```
-
-
-### 2.3 评估指标
-
-（1）检测阶段：先按照检测框和标注框的IOU评估，IOU大于某个阈值判断为检测准确。这里检测框和标注框不同于一般的通用目标检测框，是采用多边形进行表示。检测准确率：正确的检测框个数在全部检测框的占比，主要是判断检测指标。检测召回率：正确的检测框个数在全部标注框的占比，主要是判断漏检的指标。
-
-（2）识别阶段： 字符识别准确率，即正确识别的文本行占标注的文本行数量的比例，只有整行文本识别对才算正确识别。
-
-（3）端到端统计： 端对端召回率：准确检测并正确识别文本行在全部标注文本行的占比； 端到端准确率：准确检测并正确识别文本行在 检测到的文本行数量 的占比； 准确检测的标准是检测框与标注框的IOU大于某个阈值，正确识别的检测框中的文本与标注的文本相同。
-
-
-
-## 3. 数据与垂类场景
-
-
-### 3.1 训练数据
-目前开源的模型，数据集和量级如下：
-
- - 检测：
- - 英文数据集，ICDAR2015
- - 中文数据集，LSVT街景数据集训练数据3w张图片
-
- - 识别：
- - 英文数据集，MJSynth和SynthText合成数据，数据量上千万。
- - 中文数据集，LSVT街景数据集根据真值将图crop出来，并进行位置校准，总共30w张图像。此外基于LSVT的语料，合成数据500w。
- - 小语种数据集，使用不同语料和字体，分别生成了100w合成数据集，并使用ICDAR-MLT作为验证集。
-
-其中，公开数据集都是开源的，用户可自行搜索下载，也可参考[中文数据集](dataset/datasets.md)，合成数据暂不开源，用户可使用开源合成工具自行合成，可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer) 、[SynthText](https://github.com/ankush-me/SynthText) 、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) 等。
-
-
-### 3.2 垂类场景
-
-PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练；
-如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。
-
-
-
-### 3.3 自己构建数据集
-
-在构建数据集时有几个经验可供参考：
-
-（1） 训练集的数据量：
-
- a. 检测需要的数据相对较少，在PaddleOCR模型的基础上进行Fine-tune，一般需要500张可达到不错的效果。
- b. 识别分英文和中文，一般英文场景需要几十万数据可达到不错的效果，中文则需要几百万甚至更多。
-
-
-（2）当训练数据量少时，可以尝试以下三种方式获取更多的数据：
-
- a. 人工采集更多的训练数据，最直接也是最有效的方式。
- b. 基于PIL和opencv基本图像处理或者变换。例如PIL中ImageFont, Image, ImageDraw三个模块将文字写到背景中，opencv的旋转仿射变换，高斯滤波等。
- c. 利用数据生成算法合成数据，例如pix2pix或[StyleText](https://github.com/PFCCLab/StyleText)等算法。
-
-
-
-## 4. 常见问题
-
-**Q**：训练CRNN识别时，如何选择合适的网络输入shape？
-
- A：一般高度采用32，最长宽度的选择，有两种方法：
-
- （1）统计训练样本图像的宽高比分布。最大宽高比的选取考虑满足80%的训练样本。
-
- （2）统计训练样本文字数目。最长字符数目的选取考虑满足80%的训练样本。然后中文字符长宽比近似认为是1，英文认为3：1，预估一个最长宽度。
-
-**Q**：识别训练时，训练集精度已经到达90了，但验证集精度一直在70，涨不上去怎么办？
-
- A：训练集精度90，测试集70多的话，应该是过拟合了，有两个可尝试的方法：
-
- （1）加入更多的增广方式或者调大增广prob的[概率](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/rec_img_aug.py#L341)，默认为0.4。
-
- （2）调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
-
-**Q**: 识别模型训练时，loss能正常下降，但acc一直为0
-
- A：识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
-
-
-
-***
-
-具体的训练教程可点击下方链接跳转：
-
-- [文本检测模型训练](./detection.md)
-
-- [文本识别模型训练](./recognition.md)
-
-- [文本方向分类器训练](./angle_class.md)
-- [知识蒸馏](./knowledge_distillation.md)
diff --git a/doc/doc_ch/tree.md b/doc/doc_ch/tree.md
deleted file mode 100644
index cc3530b3df..0000000000
--- a/doc/doc_ch/tree.md
+++ /dev/null
@@ -1,216 +0,0 @@
-# 整体目录结构
-
-PaddleOCR 的整体目录结构介绍如下：
-
-```
-PaddleOCR
-├── configs // 配置文件，可通过 yml 文件选择模型结构并修改超参
-│ ├── cls // 方向分类器相关配置文件
-│ │ ├── cls_mv3.yml // 训练配置相关，包括骨干网络、head、loss、优化器和数据
-│ ├── det // 检测相关配置文件
-│ │ ├── det_mv3_db.yml // 训练配置
-│ │ ...
-│ └── rec // 识别相关配置文件
-│ ├── rec_mv3_none_bilstm_ctc.yml // crnn 训练配置
-│ ...
-├── deploy // 部署相关
-│ ├── android_demo // android_demo
-│ │ ...
-│ ├── cpp_infer // C++ infer
-│ │ ├── CMakeLists.txt // Cmake 文件
-│ │ ├── docs // 说明文档
-│ │ │ └── windows_vs2019_build.md
-│ │ ├── include // 头文件
-│ │ │ ├── clipper.h // clipper 库
-│ │ │ ├── config.h // 预测配置
-│ │ │ ├── ocr_cls.h // 方向分类器
-│ │ │ ├── ocr_det.h // 文字检测
-│ │ │ ├── ocr_rec.h // 文字识别
-│ │ │ ├── postprocess_op.h // 检测后处理
-│ │ │ ├── preprocess_op.h // 检测预处理
-│ │ │ └── utility.h // 工具
-│ │ ├── readme.md // 说明文档
-│ │ ├── ...
-│ │ ├── src // 源文件
-│ │ │ ├── clipper.cpp
-│ │ │ ├── config.cpp
-│ │ │ ├── main.cpp
-│ │ │ ├── ocr_cls.cpp
-│ │ │ ├── ocr_det.cpp
-│ │ │ ├── ocr_rec.cpp
-│ │ │ ├── postprocess_op.cpp
-│ │ │ ├── preprocess_op.cpp
-│ │ │ └── utility.cpp
-│ │ └── tools // 编译、执行脚本
-│ │ ├── build.sh // 编译脚本
-│ │ ├── config.txt // 配置文件
-│ │ └── run.sh // 测试启动脚本
-│ ├── docker
-│ │ └── hubserving
-│ │ ├── cpu
-│ │ │ └── Dockerfile
-│ │ ├── gpu
-│ │ │ └── Dockerfile
-│ │ ├── README_cn.md
-│ │ ├── README.md
-│ │ └── sample_request.txt
-│ ├── hubserving // hubserving
-│ │ ├── ocr_cls // 方向分类器
-│ │ │ ├── config.json // serving 配置
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py // 预测模型
-│ │ │ └── params.py // 预测参数
-│ │ ├── ocr_det // 文字检测
-│ │ │ ├── config.json // serving 配置
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py // 预测模型
-│ │ │ └── params.py // 预测参数
-│ │ ├── ocr_rec // 文字识别
-│ │ │ ├── config.json
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py
-│ │ │ └── params.py
-│ │ └── ocr_system // 系统预测
-│ │ ├── config.json
-│ │ ├── __init__.py
-│ │ ├── module.py
-│ │ └── params.py
-│ ├── imgs // 预测图片
-│ │ ├── cpp_infer_pred_12.png
-│ │ └── demo.png
-│ ├── ios_demo // ios demo
-│ │ ...
-│ ├── lite // lite 部署
-│ │ ├── cls_process.cc // 方向分类器数据处理
-│ │ ├── cls_process.h
-│ │ ├── config.txt // 检测配置参数
-│ │ ├── crnn_process.cc // crnn 数据处理
-│ │ ├── crnn_process.h
-│ │ ├── db_post_process.cc // db 数据处理
-│ │ ├── db_post_process.h
-│ │ ├── Makefile // 编译文件
-│ │ ├── ocr_db_crnn.cc // 串联预测
-│ │ ├── prepare.sh // 数据准备
-│ │ ├── readme.md // 说明文档
-│ │ ...
-│ ├── pdserving // pdserving 部署
-│ │ ├── det_local_server.py // 检测 快速版，部署方便预测速度快
-│ │ ├── det_web_server.py // 检测 完整版，稳定性高分布式部署
-│ │ ├── ocr_local_server.py // 检测+识别 快速版
-│ │ ├── ocr_web_client.py // 客户端
-│ │ ├── ocr_web_server.py // 检测+识别 完整版
-│ │ ├── readme.md // 说明文档
-│ │ ├── rec_local_server.py // 识别 快速版
-│ │ └── rec_web_server.py // 识别 完整版
-│ └── slim
-│ └── quantization // 量化相关
-│ ├── export_model.py // 导出模型
-│ ├── quant.py // 量化
-│ └── README.md // 说明文档
-├── doc // 文档教程
-│ ...
-├── ppocr // 网络核心代码
-│ ├── data // 数据处理
-│ │ ├── imaug // 图片和 label 处理代码
-│ │ │ ├── text_image_aug // 文本识别的 tia 数据扩充
-│ │ │ │ ├── __init__.py
-│ │ │ │ ├── augment.py // tia_distort,tia_stretch 和 tia_perspective 的代码
-│ │ │ │ ├── warp_mls.py
-│ │ │ ├── __init__.py
-│ │ │ ├── east_process.py // EAST 算法的数据处理步骤
-│ │ │ ├── make_border_map.py // 生成边界图
-│ │ │ ├── make_shrink_map.py // 生成收缩图
-│ │ │ ├── operators.py // 图像基本操作，如读取和归一化
-│ │ │ ├── randaugment.py // 随机数据增广操作
-│ │ │ ├── random_crop_data.py // 随机裁剪
-│ │ │ ├── rec_img_aug.py // 文本识别的数据扩充
-│ │ │ └── sast_process.py // SAST 算法的数据处理步骤
-│ │ ├── __init__.py // 构造 dataloader 相关代码
-│ │ ├── lmdb_dataset.py // 读取lmdb数据集的 dataset
-│ │ ├── simple_dataset.py // 读取文本格式存储数据集的 dataset
-│ ├── losses // 损失函数
-│ │ ├── __init__.py // 构造 loss 相关代码
-│ │ ├── cls_loss.py // 方向分类器 loss
-│ │ ├── det_basic_loss.py // 检测基础 loss
-│ │ ├── det_db_loss.py // DB loss
-│ │ ├── det_east_loss.py // EAST loss
-│ │ ├── det_sast_loss.py // SAST loss
-│ │ ├── rec_ctc_loss.py // CTC loss
-│ │ ├── rec_att_loss.py // Attention loss
-│ ├── metrics // 评估指标
-│ │ ├── __init__.py // 构造 metric 相关代码
-│ │ ├── cls_metric.py // 方向分类器 metric
-│ │ ├── det_metric.py // 检测 metric
- │ ├── eval_det_iou.py // 检测 iou 相关
-│ │ ├── rec_metric.py // 识别 metric
-│ ├── modeling // 组网相关
-│ │ ├── architectures // 网络
-│ │ │ ├── __init__.py // 构造 model 相关代码
-│ │ │ ├── base_model.py // 组网代码
-│ │ ├── backbones // 骨干网络
-│ │ │ ├── __init__.py // 构造 backbone 相关代码
-│ │ │ ├── det_mobilenet_v3.py // 检测 mobilenet_v3
-│ │ │ ├── det_resnet_vd.py // 检测 resnet
-│ │ │ ├── det_resnet_vd_sast.py // 检测 SAST算法的resnet backbone
-│ │ │ ├── rec_mobilenet_v3.py // 识别 mobilenet_v3
-│ │ │ └── rec_resnet_vd.py // 识别 resnet
-│ │ ├── necks // 颈函数
-│ │ │ ├── __init__.py // 构造 neck 相关代码
-│ │ │ ├── db_fpn.py // 标准 fpn 网络
-│ │ │ ├── east_fpn.py // EAST 算法的 fpn 网络
-│ │ │ ├── sast_fpn.py // SAST 算法的 fpn 网络
-│ │ │ ├── rnn.py // 识别 序列编码
-│ │ ├── heads // 头函数
-│ │ │ ├── __init__.py // 构造 head 相关代码
-│ │ │ ├── cls_head.py // 方向分类器 分类头
-│ │ │ ├── det_db_head.py // DB 检测头
-│ │ │ ├── det_east_head.py // EAST 检测头
-│ │ │ ├── det_sast_head.py // SAST 检测头
-│ │ │ ├── rec_ctc_head.py // 识别 ctc
-│ │ │ ├── rec_att_head.py // 识别 attention
-│ │ ├── transforms // 图像变换
-│ │ │ ├── __init__.py // 构造 transform 相关代码
-│ │ │ └── tps.py // TPS 变换
-│ ├── optimizer // 优化器
-│ │ ├── __init__.py // 构造 optimizer 相关代码
-│ │ └── learning_rate.py // 学习率衰减
-│ │ └── optimizer.py // 优化器
-│ │ └── regularizer.py // 网络正则化
-│ ├── postprocess // 后处理
-│ │ ├── cls_postprocess.py // 方向分类器 后处理
-│ │ ├── db_postprocess.py // DB 后处理
-│ │ ├── east_postprocess.py // EAST 后处理
-│ │ ├── locality_aware_nms.py // NMS
-│ │ ├── rec_postprocess.py // 识别网络 后处理
-│ │ └── sast_postprocess.py // SAST 后处理
-│ └── utils // 工具
-│ ├── dict // 小语种字典
-│ ....
-│ ├── ic15_dict.txt // 英文数字字典，区分大小写
-│ ├── ppocr_keys_v1.txt // 中文字典，用于训练中文模型
-│ ├── logging.py // logger
-│ ├── save_load.py // 模型保存和加载函数
-│ ├── stats.py // 统计
-│ └── utility.py // 工具函数
-├── tools
-│ ├── eval.py // 评估函数
-│ ├── export_model.py // 导出 inference 模型
-│ ├── infer // 基于预测引擎预测
-│ │ ├── predict_cls.py
-│ │ ├── predict_det.py
-│ │ ├── predict_rec.py
-│ │ ├── predict_system.py
-│ │ └── utility.py
-│ ├── infer_cls.py // 基于训练引擎 预测分类
-│ ├── infer_det.py // 基于训练引擎 预测检测
-│ ├── infer_rec.py // 基于训练引擎 预测识别
-│ ├── program.py // 整体流程
-│ ├── test_hubserving.py
-│ └── train.py // 启动训练
-├── paddleocr.py
-├── README_ch.md // 中文说明文档
-├── README_en.md // 英文说明文档
-├── README.md // 主页说明文档
-├── requirements.txt // 安装依赖
-├── setup.py // whl包打包脚本
-├── train.sh // 启动训练脚本
diff --git a/doc/doc_ch/update.md b/doc/doc_ch/update.md
deleted file mode 100644
index a14f5905d7..0000000000
--- a/doc/doc_ch/update.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 更新
-- 2022.5.9 发布PaddleOCR v2.5。发布内容包括：
- - [PP-OCRv3](./ppocr_introduction.md#pp-ocrv3)，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上；
- - 半自动标注工具[PPOCRLabelv2](https://github.com/PFCCLab/PPOCRLabel)：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能；
- - OCR产业落地工具集：打通22种训练部署软硬件环境与方式，覆盖企业90%的训练部署环境需求
- - 交互式OCR开源电子书[《动手学OCR》](./ocr_book.md)，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。
-- 2022.5.7 添加对[Weights & Biases](https://docs.wandb.ai/)训练日志记录工具的支持。
-- 2021.12.21 《OCR十讲》课程开讲，12月21日起每晚八点半线上授课！ 【免费】报名地址：https://aistudio.baidu.com/aistudio/course/introduce/25207
-- 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法（PSENet），3种文本识别算法（NRTR、SEED、SAR）；文档结构化算法新增1种关键信息提取算法（SDMGR），3种DocVQA算法（LayoutLM、LayoutLMv2，LayoutXLM）。
-- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
-- 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
-- 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
-- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。
-- 2020.12.15 更新数据合成工具[Style-Text](https://github.com/PFCCLab/StyleText/blob/main/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
-- 2020.12.07 [FAQ](../../doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
-- 2020.11.25 更新半自动标注工具[PPOCRLabel](https://github.com/PFCCLab/PPOCRLabel/blob/main/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
-- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
-- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见PP-OCR Pipeline)，适合在移动端部署使用。
-- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。
-- 2020.9.17 更新[英文识别模型](./models_list.md#english-recognition-model)和[多语种识别模型](./models_list.md#english-recognition-model)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
-- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./FAQ.md)
-- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md)
-- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
-- 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294)
-- 2020.7.23 发布7月21日B站直播课回放和PPT，课节1，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
-- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
-- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
-- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
-- 2020.7.9 添加支持空格的识别模型，识别效果，预测及训练方式请参考快速开始和文本识别训练相关文档
-- 2020.7.9 添加数据增强、学习率衰减策略,具体参考[配置文件](./config.md)
-- 2020.6.8 添加[数据集](dataset/datasets.md)，并保持持续更新
-- 2020.6.5 支持 `attetnion` 模型导出 `inference_model`
-- 2020.6.5 支持单独预测识别时，输出结果得分
-- 2020.5.30 提供超轻量级中文OCR在线体验
-- 2020.5.30 模型预测、训练支持Windows系统
-- 2020.5.30 开源通用中文OCR模型
-- 2020.5.14 发布[PaddleOCR公开课](https://www.bilibili.com/video/BV1nf4y1U7RX?p=4)
-- 2020.5.14 发布[PaddleOCR实战练习](https://aistudio.baidu.com/aistudio/projectdetail/467229)
-- 2020.5.14 开源8.6M超轻量级中文OCR模型
diff --git a/doc/doc_ch/visualization.md b/doc/doc_ch/visualization.md
deleted file mode 100644
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+++ /dev/null
@@ -1,59 +0,0 @@
-# 效果展示
-
-
-
-## 超轻量PP-OCRv3效果展示
-
-### PP-OCRv3中文模型
-
-
-### PP-OCRv3英文数字模型
-
-
-
-### PP-OCRv3多语言模型
-
-
-
-
- 
- 
- 
-
-
-
-## 通用PP-OCR server 效果展示
-
-
-
-
-
-## 英文识别模型效果展示
-
- 
-
- 
- 
-
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-
- 
-
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- 
-
- 
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- 
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-
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-
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-
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg | Download |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|
-| CPPD Tiny | 97.1 | 94.4 | 96.6 | 86.6 | 88.5 | 90.3 | 92.25 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_tiny_en_train.tar) |
-| CPPD Base | 98.2 | 95.5 | 97.6 | 87.9 | 90.0 | 92.7 | 93.80 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar)|
-| CPPD Base 48*160 | 97.5 | 95.5 | 97.7 | 87.7 | 92.4 | 93.7 | 94.10 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_48_160_en_train.tar) |
-
-* Trained on Synth dataset(MJ+ST), Test on Union14M-L benchmark from [U14m](https://github.com/Mountchicken/Union14M/).
-
-| Model |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | Download |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
-| CPPD Tiny | 52.4 | 12.3 | 48.2 | 54.4 | 61.5 | 53.4 | 61.4 | 49.10 | Same as the table above. |
-| CPPD Base | 65.5 | 18.6 | 56.0 | 61.9 | 71.0 | 57.5 | 65.8 | 56.63 | Same as the table above. |
-| CPPD Base 48*160 | 71.9 | 22.1 | 60.5 | 67.9 | 78.3 | 63.9 | 67.1 | 61.69 | Same as the table above. |
-
-* Trained on Union14M-L training dataset.
-
-| Model |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg | Download |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|
-| CPPD Base 32*128 | 98.7 | 98.5 | 99.4 | 91.7 | 96.7 | 99.7 | 97.44 | [en](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_u14m_train.tar) |
-
-| Model |Curve | Multi-
Oriented |Artistic |Contextless| Salient | Multi-
word | General | Avg | Download |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
-| CPPD Base 32*128 | 87.5 | 70.7 | 78.2 | 82.9 | 85.5 | 85.4 | 84.3 | 82.08 | Same as the table above. |
-
-* Chinese dataset from [Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition).
-
-| Model | Scene | Web | Document | Handwriting | Avg | Download |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|
-| CPPD Base | 74.4 | 76.1 | 98.6 | 55.3 | 76.10 | [ch](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_ch_train.tar) |
-| CPPD Base + STN | 78.4 | 79.3 | 98.9 | 57.6 | 78.55 | [ch](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_stn_ch_train.tar) |
-
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-#### Dataset Preparation
-
-[English dataset download](https://github.com/baudm/parseq)
-[Union14M-Benchmark download](https://github.com/Mountchicken/Union14M)
-[Chinese dataset download](https://github.com/fudanvi/benchmarking-chinese-text-recognition#download)
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_svtrnet_cppd_base_en.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_svtrnet_cppd_base_en.yml
-```
-
-Evaluation:
-
-You can download the model files and configuration files provided by `CPPD`: [download link](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar), take `CPPD-B` as an example, using the following command to evaluate:
-
-```
-# Download the tar archive containing the model files and configuration files of CPPD-B and extract it
-wget https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar && tar xf rec_svtr_cppd_base_en_train.tar
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c ./rec_svtr_cppd_base_en_train/rec_svtrnet_cppd_base_en.yml -o Global.pretrained_model=./rec_svtr_cppd_base_en_train/best_model
-```
-
-Prediction:
-
-```
-python3 tools/infer_rec.py -c ./rec_svtr_cppd_base_en_train/rec_svtrnet_cppd_base_en.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_svtr_cppd_base_en_train/best_model
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the CPPD text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar) ), you can use the following command to convert:
-
-```
-# export model
-# en
-python3 tools/export_model.py -c configs/rec/rec_svtrnet_cppd_base_en.yml -o Global.pretrained_model=./rec_svtr_cppd_base_en_train/best_model.pdparams Global.save_inference_dir=./rec_svtr_cppd_base_en_infer
-# ch
-python3 tools/export_model.py -c configs/rec/rec_svtrnet_cppd_base_ch.yml -o Global.pretrained_model=./rec_svtr_cppd_base_ch_train/best_model.pdparams Global.save_inference_dir=./rec_svtr_cppd_base_ch_infer
-
-# speed test
-# docker image https://hub.docker.com/r/paddlepaddle/paddle/tags/: sudo docker pull paddlepaddle/paddle:2.4.2-gpu-cuda11.2-cudnn8.2-trt8.0
-# install auto_log: pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
-# en
-python3 tools/infer/predict_rec.py --image_dir='../iiik' --rec_model_dir='./rec_svtr_cppd_base_en_infer/' --rec_algorithm='CPPD' --rec_image_shape='3,32,100' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --warmup=True --benchmark=True --rec_batch_num=1 --use_tensorrt=True
-# ch
-python3 tools/infer/predict_rec.py --image_dir='../iiik' --rec_model_dir='./rec_svtr_cppd_base_ch_infer/' --rec_algorithm='CPPDPadding' --rec_image_shape='3,32,256' --warmup=True --benchmark=True --rec_batch_num=1 --use_tensorrt=True
-# stn_ch
-python3 tools/infer/predict_rec.py --image_dir='../iiik' --rec_model_dir='./rec_svtr_cppd_base_stn_ch_infer/' --rec_algorithm='CPPD' --rec_image_shape='3,64,256' --warmup=True --benchmark=True --rec_batch_num=1 --use_tensorrt=True
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-
-After the conversion is successful, there are three files in the directory:
-```
-/inference/rec_svtr_cppd_base_en_infer/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## Citation
-
-```bibtex
-@article{Du2023CPPD,
- title = {Context Perception Parallel Decoder for Scene Text Recognition},
- author = {Du, Yongkun and Chen, Zhineng and Jia, Caiyan and Yin, Xiaoting and Li, Chenxia and Du, Yuning and Jiang, Yu-Gang},
- booktitle = {Arxiv},
- year = {2023},
- url = {https://arxiv.org/abs/2307.12270}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_crnn_en.md b/doc/doc_en/algorithm_rec_crnn_en.md
deleted file mode 100644
index 8548c2fa62..0000000000
--- a/doc/doc_en/algorithm_rec_crnn_en.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# CRNN
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
-
-> Baoguang Shi, Xiang Bai, Cong Yao
-
-> IEEE, 2015
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|ACC|config|Download link|
-| --- | --- | --- | --- | --- |
-|---|---|---|---|---|
-|CRNN|Resnet34_vd|81.04%|[configs/rec/rec_r34_vd_none_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
-|CRNN|MobileNetV3|77.95%|[configs/rec/rec_mv3_none_bilstm_ctc.yml](../../configs/rec/rec_mv3_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the CRNN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_CRNN_train.tar) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
-```
-
-For CRNN text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
-```
-
-
-### 4.2 C++ Inference
-
-With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
-
-
-
-### 4.3 Serving
-
-With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
-
-
-
-### 4.4 More
-
-More deployment schemes supported for CRNN:
-
-- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
-
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@ARTICLE{7801919,
- author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
- journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
- title={An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition},
- year={2017},
- volume={39},
- number={11},
- pages={2298-2304},
- doi={10.1109/TPAMI.2016.2646371}}
-```
diff --git a/doc/doc_en/algorithm_rec_nrtr_en.md b/doc/doc_en/algorithm_rec_nrtr_en.md
deleted file mode 100644
index 309d7ab123..0000000000
--- a/doc/doc_en/algorithm_rec_nrtr_en.md
+++ /dev/null
@@ -1,270 +0,0 @@
-# NRTR
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-- [6. Release Note](#6)
-
-
-## 1. Introduction
-
-Paper:
-> [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://arxiv.org/abs/1806.00926)
-> Fenfen Sheng and Zhineng Chen and Bo Xu
-> ICDAR, 2019
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|NRTR|MTB|[rec_mtb_nrtr.yml](../../configs/rec/rec_mtb_nrtr.yml)|84.21%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)|
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_mtb_nrtr.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_mtb_nrtr.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_mtb_nrtr.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the NRTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy Global.save_inference_dir=./inference/rec_mtb_nrtr
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-- If you modified the input size during training, please modify the `infer_shape` corresponding to NRTR in the `tools/export_model.py` file.
-
-After the conversion is successful, there are three files in the directory:
-```
-/inference/rec_mtb_nrtr/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-For NRTR text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_mtb_nrtr/' --rec_algorithm='NRTR' --rec_image_shape='1,32,100' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt'
-```
-
-
-
-After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
-The result is as follows:
-```shell
-Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-1. In the `NRTR` paper, Beam search is used to decode characters, but the speed is slow. Beam search is not used by default here, and greedy search is used to decode characters.
-
-
-## 6. Release Note
-
-1. The release/2.6 version updates the NRTR code structure. The new version of NRTR can load the model parameters of the old version (release/2.5 and before), and you may use the following code to convert the old version model parameters to the new version model parameters:
-
-```python
-
- params = paddle.load('path/' + '.pdparams') # the old version parameters
- state_dict = model.state_dict() # the new version model parameters
- new_state_dict = {}
-
- for k1, v1 in state_dict.items():
-
- k = k1
- if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
-
- k_para = k[:13] + 'layers.' + k[13:]
- q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
- k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
- v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
-
- new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
-
- elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
-
- k_para = k[:13] + 'layers.' + k[13:]
- q = params[k_para.replace('qkv', 'conv1')]
- k = params[k_para.replace('qkv', 'conv2')]
- v = params[k_para.replace('qkv', 'conv3')]
-
- new_state_dict[k1] = np.concatenate([q, k, v], -1)
-
- elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k:
-
- k_para = k[:13] + 'layers.' + k[13:]
- new_state_dict[k1] = params[k_para]
-
- elif 'encoder' in k and 'norm3' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')]
-
- elif 'encoder' in k and 'norm1' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- new_state_dict[k1] = params[k_para]
-
-
- elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
- k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
- v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
- new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
-
- elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- q = params[k_para.replace('qkv', 'conv1')]
- k = params[k_para.replace('qkv', 'conv2')]
- v = params[k_para.replace('qkv', 'conv3')]
- new_state_dict[k1] = np.concatenate([q, k, v], -1)
-
- elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k:
-
- k_para = k[:13] + 'layers.' + k[13:]
- new_state_dict[k1] = params[k_para]
-
- elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('cross_attn', 'multihead_attn')
- q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3))
- new_state_dict[k1] = q[:, :, 0, 0]
-
- elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('cross_attn', 'multihead_attn')
- q = params[k_para.replace('q', 'conv1')]
- new_state_dict[k1] = q
-
- elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('cross_attn', 'multihead_attn')
- k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3))
- v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3))
- new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1)
-
- elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('cross_attn', 'multihead_attn')
- k = params[k_para.replace('kv', 'conv2')]
- v = params[k_para.replace('kv', 'conv3')]
- new_state_dict[k1] = np.concatenate([k, v], -1)
-
- elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k:
-
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('cross_attn', 'multihead_attn')
- new_state_dict[k1] = params[k_para]
- elif 'decoder' in k and 'norm' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- new_state_dict[k1] = params[k_para]
- elif 'mlp' in k and 'weight' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('fc', 'conv')
- k_para = k_para.replace('mlp.', '')
- w = params[k_para].transpose((1, 0, 2, 3))
- new_state_dict[k1] = w[:, :, 0, 0]
- elif 'mlp' in k and 'bias' in k:
- k_para = k[:13] + 'layers.' + k[13:]
- k_para = k_para.replace('fc', 'conv')
- k_para = k_para.replace('mlp.', '')
- w = params[k_para]
- new_state_dict[k1] = w
-
- else:
- new_state_dict[k1] = params[k1]
-
- if list(new_state_dict[k1].shape) != list(v1.shape):
- print(k1)
-
-
- for k, v1 in state_dict.items():
- if k not in new_state_dict.keys():
- print(1, k)
- elif list(new_state_dict[k].shape) != list(v1.shape):
- print(2, k)
-
-
-
- model.set_state_dict(new_state_dict)
- paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams')
-
-```
-
-2. The new version has a clean code structure and improved inference speed compared with the old version.
-
-## Citation
-
-```bibtex
-@article{Sheng2019NRTR,
- title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
- author = {Fenfen Sheng and Zhineng Chen and Bo Xu},
- booktitle = {ICDAR},
- year = {2019},
- url = {http://arxiv.org/abs/1806.00926},
- pages = {781-786}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_parseq_en.md b/doc/doc_en/algorithm_rec_parseq_en.md
deleted file mode 100644
index 618095ec75..0000000000
--- a/doc/doc_en/algorithm_rec_parseq_en.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# PasreQ
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://arxiv.org/abs/2207.06966)
-> Darwin Bautista, Rowel Atienza
-> ECCV, 2021
-
-Using real datasets (real) and synthetic datsets (synth) for training respectively，and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets.
-- The real datasets include COCO-Text, RCTW17, Uber-Text, ArT, LSVT, MLT19, ReCTS, TextOCR and OpenVINO datasets.
-- The synthesis datasets include MJSynth and SynthText datasets.
-
-the algorithm reproduction effect is as follows:
-
-|Training Dataset|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- | --- |
-|Synth|ParseQ|VIT|[rec_vit_parseq.yml](../../configs/rec/rec_vit_parseq.yml)|91.24%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_synth.tgz)|
-|Real|ParseQ|VIT|[rec_vit_parseq.yml](../../configs/rec/rec_vit_parseq.yml)|94.74%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_real.tgz)|
-|||||||
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_vit_parseq.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_vit_parseq.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SAR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_real.tgz) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model=./rec_vit_parseq_real/best_accuracy Global.save_inference_dir=./inference/rec_parseq
-```
-
-For SAR text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_parseq/" --rec_image_shape="3, 32, 128" --rec_algorithm="ParseQ" --rec_char_dict_path="ppocr/utils/dict/parseq_dict.txt" --max_text_length=25 --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@InProceedings{bautista2022parseq,
- title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
- author={Bautista, Darwin and Atienza, Rowel},
- booktitle={European Conference on Computer Vision},
- pages={178--196},
- month={10},
- year={2022},
- publisher={Springer Nature Switzerland},
- address={Cham},
- doi={10.1007/978-3-031-19815-1_11},
- url={https://doi.org/10.1007/978-3-031-19815-1_11}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_rare_en.md b/doc/doc_en/algorithm_rec_rare_en.md
deleted file mode 100644
index a756ac75b3..0000000000
--- a/doc/doc_en/algorithm_rec_rare_en.md
+++ /dev/null
@@ -1,119 +0,0 @@
-# RARE
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper information:
-> [Robust Scene Text Recognition with Automatic Rectification](https://arxiv.org/abs/1603.03915v2)
-> Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai∗
-> CVPR, 2016
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Models|Backbone Networks|Configuration Files|Avg Accuracy|Download Links|
-| --- | --- | --- | --- | --- |
-|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.60%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
-|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.50%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
-
-
-
-## 2. Environment
-Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
-
-
-### 3.1 Training
-
-````
-#Single card training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
-#Multi-card training, specify the card number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
-````
-
-
-### 3.2 Evaluation
-
-````
-# GPU evaluation, Global.pretrained_model is the model to be evaluated
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-````
-
-
-### 3.3 Prediction
-
-````
-python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-````
-
-
-## 4. Inference
-
-
-### 4.1 Python Inference
-First, convert the model saved during the RARE text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ([Model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar) ), which can be converted using the following command:
-
-```shell
-python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rare
-````
-
-RARE text recognition model inference, you can execute the following commands:
-
-```shell
-python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rare/" --rec_image_shape="3, 32, 100" --rec_char_dict_path= "./ppocr/utils/ic15_dict.txt"
-````
-The inference results are as follows:
-
-
-
-````
-Predicts of doc/imgs_words/en/word_1.png:('joint ', 0.9999969601631165)
-````
-
-
-### 4.2 C++ Inference
-
-Not currently supported
-
-
-### 4.3 Serving
-
-Not currently supported
-
-
-### 4.4 More
-
-The RARE model also supports the following inference deployment methods:
-
-- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
-
-
-## 5. FAQ
-
-## Quote
-
-````bibtex
-@inproceedings{2016Robust,
- title={Robust Scene Text Recognition with Automatic Rectification},
- author={ Shi, B. and Wang, X. and Lyu, P. and Cong, Y. and Xiang, B. },
- booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year={2016},
-}
-````
diff --git a/doc/doc_en/algorithm_rec_rfl_en.md b/doc/doc_en/algorithm_rec_rfl_en.md
deleted file mode 100644
index 36a93cb59e..0000000000
--- a/doc/doc_en/algorithm_rec_rfl_en.md
+++ /dev/null
@@ -1,143 +0,0 @@
-# RFL
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition](https://arxiv.org/abs/2105.06229.pdf)
-> Hui Jiang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Wenqi Ren, Fei Wu, and Wenming Tan
-> ICDAR, 2021
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|RFL-CNT|ResNetRFL|[rec_resnet_rfl_visual.yml](../../configs/rec/rec_resnet_rfl_visual.yml)|93.40%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_visual_train.tar)|
-|RFL-Att|ResNetRFL|[rec_resnet_rfl_att.yml](../../configs/rec/rec_resnet_rfl_att.yml)|88.63%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar)|
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#step1:train the CNT branch
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
-
-#step2:joint training of CNT and Att branches
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-
-
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the RFL text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_resnet_rfl_att
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-- If you modified the input size during training, please modify the `infer_shape` corresponding to NRTR in the `tools/export_model.py` file.
-
-After the conversion is successful, there are three files in the directory:
-```
-/inference/rec_resnet_rfl_att/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-For RFL text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_resnet_rfl_att/' --rec_algorithm='RFL' --rec_image_shape='1,32,100'
-```
-
-
-
-After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
-The result is as follows:
-```shell
-Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999927282333374)
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-## Citation
-
-```bibtex
-@article{2021Reciprocal,
- title = {Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition},
- author = {Jiang, H. and Xu, Y. and Cheng, Z. and Pu, S. and Niu, Y. and Ren, W. and Wu, F. and Tan, W. },
- booktitle = {ICDAR},
- year = {2021},
- url = {https://arxiv.org/abs/2105.06229}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_robustscanner_en.md b/doc/doc_en/algorithm_rec_robustscanner_en.md
deleted file mode 100644
index 99372d5130..0000000000
--- a/doc/doc_en/algorithm_rec_robustscanner_en.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# RobustScanner
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition](https://arxiv.org/pdf/2007.07542.pdf)
-> Xiaoyu Yue, Zhanghui Kuang, Chenhao Lin, Hongbin Sun, Wayne
-Zhang
-> ECCV, 2020
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|RobustScanner|ResNet31|[rec_r31_robustscanner.yml](../../configs/rec/rec_r31_robustscanner.yml)|87.77%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
-
-Note:In addition to using the two text recognition datasets MJSynth and SynthText, [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg) data (extraction code: 627x), and some real data are used in training, the specific data details can refer to the paper.
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r31_robustscanner.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r31_robustscanner.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r31_robustscanner.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r31_robustscanner.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r31_robustscanner.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r31_robustscanner
-```
-
-For RobustScanner text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r31_robustscanner/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="RobustScanner" --rec_char_dict_path="ppocr/utils/dict90.txt" --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@article{2020RobustScanner,
- title={RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition},
- author={Xiaoyu Yue and Zhanghui Kuang and Chenhao Lin and Hongbin Sun and Wayne Zhang},
- journal={ECCV2020},
- year={2020},
-}
-```
diff --git a/doc/doc_en/algorithm_rec_rosetta_en.md b/doc/doc_en/algorithm_rec_rosetta_en.md
deleted file mode 100644
index 2a1d7b3127..0000000000
--- a/doc/doc_en/algorithm_rec_rosetta_en.md
+++ /dev/null
@@ -1,121 +0,0 @@
-# Rosetta
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper information:
-> [Rosetta: Large Scale System for Text Detection and Recognition in Images](https://arxiv.org/abs/1910.05085)
-> Borisyuk F , Gordo A , V Sivakumar
-> KDD, 2018
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Models|Backbone Networks|Configuration Files|Avg Accuracy|Download Links|
-| --- | --- | --- | --- | --- |
-|Rosetta|Resnet34_vd|[configs/rec/rec_r34_vd_none_none_ctc.yml](../../configs/rec/rec_r34_vd_none_none_ctc.yml)|79.11%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
-|Rosetta|MobileNetV3|[configs/rec/rec_mv3_none_none_ctc.yml](../../configs/rec/rec_mv3_none_none_ctc.yml)|75.80%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
-
-
-
-## 2. Environment
-Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
-
-
-### 3.1 Training
-
-````
-#Single card training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
-#Multi-card training, specify the card number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_none_ctc.yml
-````
-
-
-### 3.2 Evaluation
-
-````
-# GPU evaluation, Global.pretrained_model is the model to be evaluated
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-````
-
-
-### 3.3 Prediction
-
-````
-python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-````
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, convert the model saved during the Rosetta text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example ( [Model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar) ), which can be converted using the following command:
-
-```shell
-python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_none_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_none_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rosetta
-````
-
-Rosetta text recognition model inference, you can execute the following commands:
-
-```shell
-python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rosetta/" --rec_image_shape="3, 32, 100" --rec_char_dict_path= "./ppocr/utils/ic15_dict.txt"
-````
-
-The inference results are as follows:
-
-
-
-````
-Predicts of doc/imgs_words/en/word_1.png:('joint', 0.9999982714653015)
-````
-
-
-### 4.2 C++ Inference
-
-Not currently supported
-
-
-### 4.3 Serving
-
-Not currently supported
-
-
-### 4.4 More
-
-The Rosetta model also supports the following inference deployment methods:
-
-- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
-
-
-## 5. FAQ
-
-## Quote
-
-````bibtex
-@inproceedings{2018Rosetta,
- title={Rosetta: Large Scale System for Text Detection and Recognition in Images},
- author={ Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath },
- booktitle={the 24th ACM SIGKDD International Conference},
- year={2018},
-}
-````
diff --git a/doc/doc_en/algorithm_rec_sar_en.md b/doc/doc_en/algorithm_rec_sar_en.md
deleted file mode 100644
index 5c8319da3b..0000000000
--- a/doc/doc_en/algorithm_rec_sar_en.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# SAR
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://arxiv.org/abs/1811.00751)
-> Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang
-> AAAI, 2019
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|SAR|ResNet31|[rec_r31_sar.yml](../../configs/rec/rec_r31_sar.yml)|87.20%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar)|
-
-Note:In addition to using the two text recognition datasets MJSynth and SynthText, [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg) data (extraction code: 627x), and some real data are used in training, the specific data details can refer to the paper.
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r31_sar.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r31_sar.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r31_sar.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r31_sar.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SAR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r31_sar.yml -o Global.pretrained_model=./rec_r31_sar_train/best_accuracy Global.save_inference_dir=./inference/rec_sar
-```
-
-For SAR text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@article{Li2019ShowAA,
- title={Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition},
- author={Hui Li and Peng Wang and Chunhua Shen and Guyu Zhang},
- journal={ArXiv},
- year={2019},
- volume={abs/1811.00751}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_satrn_en.md b/doc/doc_en/algorithm_rec_satrn_en.md
deleted file mode 100644
index acc95ac035..0000000000
--- a/doc/doc_en/algorithm_rec_satrn_en.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# SATRN
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-论文信息：
-> [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396)
-> Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee
-> CVPR, 2020
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|SATRN|ShallowCNN|88.05%|[configs/rec/rec_satrn.yml](../../configs/rec/rec_satrn.yml)|[训练模型](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a)|
-
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_satrn.yml
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_satrn.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SATRN text recognition training process is converted into an inference model. ( [Model download link](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model=./rec_satrn_train/best_accuracy Global.save_inference_dir=./inference/rec_satrn
-```
-
-For SATRN text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_satrn/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SATRN" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-## 引用
-
-```bibtex
-@article{lee2019recognizing,
- title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
- author={Junyeop Lee and Sungrae Park and Jeonghun Baek and Seong Joon Oh and Seonghyeon Kim and Hwalsuk Lee},
- year={2019},
- eprint={1910.04396},
- archivePrefix={arXiv},
- primaryClass={cs.CV}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_seed_en.md b/doc/doc_en/algorithm_rec_seed_en.md
deleted file mode 100644
index 83cadfceac..0000000000
--- a/doc/doc_en/algorithm_rec_seed_en.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# SEED
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
-
-> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
-
-> CVPR, 2020
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|ACC|config|Download link|
-| --- | --- | --- | --- | --- |
-|SEED|Aster_Resnet| 85.20% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-The SEED model needs to additionally load the [language model](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) trained by FastText, and install the fasttext dependencies:
-
-```
-python3 -m pip install fasttext==0.9.1
-```
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-
-Not support
-
-
-### 4.2 C++ Inference
-
-Not support
-
-
-### 4.3 Serving
-
-Not support
-
-
-### 4.4 More
-
-Not support
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@inproceedings{qiao2020seed,
- title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
- author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
- booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
- pages={13528--13537},
- year={2020}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_spin_en.md b/doc/doc_en/algorithm_rec_spin_en.md
deleted file mode 100644
index 3aea580976..0000000000
--- a/doc/doc_en/algorithm_rec_spin_en.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition](https://arxiv.org/abs/2005.13117)
-> Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou
-> AAAI, 2020
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets. The algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.00%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
-
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SPIN text recognition training process is converted into an inference model. you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att
-```
-
-For SPIN text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@article{2020SPIN,
- title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition},
- author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou},
- journal={AAAI2020},
- year={2020},
-}
-```
diff --git a/doc/doc_en/algorithm_rec_srn_en.md b/doc/doc_en/algorithm_rec_srn_en.md
deleted file mode 100644
index 1d7fc07dc2..0000000000
--- a/doc/doc_en/algorithm_rec_srn_en.md
+++ /dev/null
@@ -1,113 +0,0 @@
-# SRN
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://arxiv.org/abs/2003.12294#)
-> Deli Yu, Xuan Li, Chengquan Zhang, Junyu Han, Jingtuo Liu, Errui Ding
-> CVPR,2020
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|SRN|Resnet50_vd_fpn|[rec_r50_fpn_srn.yml](../../configs/rec/rec_r50_fpn_srn.yml)|86.31%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
-
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r50_fpn_srn.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r50_fpn_srn.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SRN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model=./rec_r50_vd_srn_train/best_accuracy Global.save_inference_dir=./inference/rec_srn
-```
-
-For SRN text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_srn/" --rec_image_shape="1,64,256" --rec_char_type="ch" --rec_algorithm="SRN" --rec_char_dict_path="ppocr/utils/ic15_dict.txt" --use_space_char=False
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-
-## Citation
-
-```bibtex
-@article{Yu2020TowardsAS,
- title={Towards Accurate Scene Text Recognition With Semantic Reasoning Networks},
- author={Deli Yu and Xuan Li and Chengquan Zhang and Junyu Han and Jingtuo Liu and Errui Ding},
- journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
- year={2020},
- pages={12110-12119}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_starnet.md b/doc/doc_en/algorithm_rec_starnet.md
deleted file mode 100644
index 45802c8939..0000000000
--- a/doc/doc_en/algorithm_rec_starnet.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# STAR-Net
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper information:
-> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
-> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
-> BMVC, pages 43.1-43.13, 2016
-
-Refer to [DTRB](https://arxiv.org/abs/1904.01906) text Recognition Training and Evaluation Process . Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Models|Backbone Networks|Avg Accuracy|Configuration Files|Download Links|
-| --- | --- | --- | --- | --- |
-|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
-|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[ trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
-
-
-
-## 2. Environment
-Please refer to [Operating Environment Preparation](./environment_en.md) to configure the PaddleOCR operating environment, and refer to [Project Clone](./clone_en.md) to clone the project code.
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Training Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Take the backbone network based on Resnet34_vd as an example:
-
-
-### 3.1 Training
-After the data preparation is complete, the training can be started. The training command is as follows:
-
-````
-#Single card training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml #Multi-card training, specify the card number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
- ````
-
-
-### 3.2 Evaluation
-
-````
-# GPU evaluation, Global.pretrained_model is the model to be evaluated
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
- ````
-
-
-### 3.3 Prediction
-
-````
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
- ````
-
-
-## 4. Inference
-
-
-### 4.1 Python Inference
-First, convert the model saved during the STAR-Net text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example [Model download address]( https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) , which can be converted using the following command:
-
-```shell
-python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet
- ````
-
-STAR-Net text recognition model inference, you can execute the following commands:
-
-```shell
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
- ````
-
-
-
-The inference results are as follows:
-
-
-```bash
-Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
-```
-
-**Attention** Since the above model refers to the [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the ultra-lightweight Chinese recognition model training in two aspects:
-
-- The image resolutions used during training are different. The image resolutions used for training the above models are [3, 32, 100], while for Chinese model training, in order to ensure the recognition effect of long texts, the image resolutions used during training are [ 3, 32, 320]. The default shape parameter of the predictive inference program is the image resolution used for training Chinese, i.e. [3, 32, 320]. Therefore, when inferring the above English model here, it is necessary to set the shape of the recognized image through the parameter rec_image_shape.
-
-- Character list, the experiment in the DTRB paper is only for 26 lowercase English letters and 10 numbers, a total of 36 characters. All uppercase and lowercase characters are converted to lowercase characters, and characters not listed above are ignored and considered spaces. Therefore, there is no input character dictionary here, but a dictionary is generated by the following command. Therefore, the parameter rec_char_dict_path needs to be set during inference, which is specified as an English dictionary "./ppocr/utils/ic15_dict.txt".
-
-```
-self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-dict_character = list(self.character_str)
-
-
- ```
-
-
-### 4.2 C++ Inference
-
-After preparing the inference model, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial to operate.
-
-
-### 4.3 Serving
-
-After preparing the inference model, refer to the [pdserving](../../deploy/pdserving/) tutorial for Serving deployment, including two modes: Python Serving and C++ Serving.
-
-
-### 4.4 More
-
-The STAR-Net model also supports the following inference deployment methods:
-
-- Paddle2ONNX Inference: After preparing the inference model, refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
-
-
-## 5. FAQ
-
-## Quote
-
-```bibtex
-@inproceedings{liu2016star,
- title={STAR-Net: a spatial attention residue network for scene text recognition.},
- author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
- booktitle={BMVC},
- volume={2},
- pages={7},
- year={2016}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_svtr_en.md b/doc/doc_en/algorithm_rec_svtr_en.md
deleted file mode 100644
index d22fe73e6f..0000000000
--- a/doc/doc_en/algorithm_rec_svtr_en.md
+++ /dev/null
@@ -1,161 +0,0 @@
-# SVTR
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [SVTR: Scene Text Recognition with a Single Visual Model](https://arxiv.org/abs/2205.00159)
-> Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang
-> IJCAI, 2022
-
-
-The accuracy (%) and model files of SVTR on the public dataset of scene text recognition are as follows:
-* Chinese dataset from [Chinese Benckmark](https://arxiv.org/abs/2112.15093) , and the Chinese training evaluation strategy of SVTR follows the paper.
-
-| Model |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg_6 |IC15
2077 |IC13
1015 |IC03
867|IC03
860|Avg_10 | Chinese
scene_test| Download link |
-|:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|:-----:|:-----:|:---------------------------------------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| SVTR Tiny | 96.85 | 91.34 | 94.53 | 83.99 | 85.43 | 89.24 | 90.87 | 80.55 | 95.37 | 95.27 | 95.70 | 90.13 | 67.90 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_ch_train.tar) |
-| SVTR Small | 95.92 | 93.04 | 95.03 | 84.70 | 87.91 | 92.01 | 91.63 | 82.72 | 94.88 | 96.08 | 96.28 | 91.02 | 69.00 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_small_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_small_none_ctc_ch_train.tar) |
-| SVTR Base | 97.08 | 91.50 | 96.03 | 85.20 | 89.92 | 91.67 | 92.33 | 83.73 | 95.66 | 95.62 | 95.81 | 91.61 | 71.40 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_base_none_ctc_en_train.tar) / - |
-| SVTR Large | 97.20 | 91.65 | 96.30 | 86.58 | 88.37 | 95.14 | 92.82 | 84.54 | 96.35 | 96.54 | 96.74 | 92.24 | 72.10 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_large_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_large_none_ctc_ch_train.tar) |
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-#### Dataset Preparation
-
-[English dataset download](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)
-[Chinese dataset download](https://github.com/fudanvi/benchmarking-chinese-text-recognition#download)
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_svtrnet.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_svtrnet.yml
-```
-
-Evaluation:
-
-You can download the model files and configuration files provided by `SVTR`: [download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar), take `SVTR-T` as an example, using the following command to evaluate:
-
-```
-# Download the tar archive containing the model files and configuration files of SVTR-T and extract it
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar && tar xf rec_svtr_tiny_none_ctc_en_train.tar
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny_6local_6global_stn_en.yml -o Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy
-```
-
-Prediction:
-
-```
-python3 tools/infer_rec.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny_6local_6global_stn_en.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the SVTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_svtrnet.yml -o Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy Global.save_inference_dir=./inference/rec_svtr_tiny_stn_en
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-
-After the conversion is successful, there are three files in the directory:
-```
-/inference/rec_svtr_tiny_stn_en/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-For SVTR text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_svtr_tiny_stn_en/' --rec_algorithm='SVTR' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt'
-```
-
-
-
-After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
-The result is as follows:
-```shell
-Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999998807907104)
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-- 1. Speed situation on CPU and GPU
- - Since most of the operators used by `SVTR` are matrix multiplication, in the GPU environment, the speed has an advantage, but in the environment where mkldnn is enabled on the CPU, `SVTR` has no advantage over the optimized convolutional network.
-- 2. SVTR model convert to ONNX failed
- - Ensure `paddle2onnx` and `onnxruntime` versions are up to date, refer to [SVTR model to onnx step-by-step example](https://github.com/PaddlePaddle/PaddleOCR/issues/7821#issuecomment-) for the convert onnx command. 1271214273).
-- 3. SVTR model convert to ONNX is successful but the inference result is incorrect
- - The possible reason is that the model parameter `out_char_num` is not set correctly, it should be set to W//4, W//8 or W//12, please refer to [Section 3.3.3 of SVTR, a high-precision Chinese scene text recognition model](https://aistudio.baidu.com/aistudio/) projectdetail/5073182?contributionType=1).
-- 4. Optimization of long text recognition
- - Refer to [Section 3.3 of SVTR, a high-precision Chinese scene text recognition model](https://aistudio.baidu.com/aistudio/projectdetail/5073182?contributionType=1).
-- 5. Notes on the reproduction of the paper results
- - Dataset using provided by [ABINet](https://github.com/FangShancheng/ABINet).
- - By default, 4 cards of GPUs are used for training, the default Batchsize of a single card is 512, and the total Batchsize is 2048, corresponding to a learning rate of 0.0005. When modifying the Batchsize or changing the number of GPU cards, the learning rate should be modified in equal proportion.
-- 6. Exploration Directions for further optimization
- - Learning rate adjustment: adjusting to twice the default to keep Batchsize unchanged; or reducing Batchsize to 1/2 the default to keep the learning rate unchanged.
- - Data augmentation strategies: optionally `RecConAug` and `RecAug`.
- - If STN is not used, `Local` of `mixer` can be replaced by `Conv` and `local_mixer` can all be modified to `[5, 5]`.
- - Grid search for optimal `embed_dim`, `depth`, `num_heads` configurations.
- - Use the `Post-Normalization strategy`, which is to modify the model configuration `prenorm` to `True`.
-
-## Citation
-
-```bibtex
-@article{Du2022SVTR,
- title = {SVTR: Scene Text Recognition with a Single Visual Model},
- author = {Du, Yongkun and Chen, Zhineng and Jia, Caiyan and Yin, Xiaoting and Zheng, Tianlun and Li, Chenxia and Du, Yuning and Jiang, Yu-Gang},
- booktitle = {IJCAI},
- year = {2022},
- url = {https://arxiv.org/abs/2205.00159}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_visionlan_en.md b/doc/doc_en/algorithm_rec_visionlan_en.md
deleted file mode 100644
index 585e853913..0000000000
--- a/doc/doc_en/algorithm_rec_visionlan_en.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# VisionLAN
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://arxiv.org/abs/2108.09661)
-> Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang
-> ICCV, 2021
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.30%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r45_visionlan.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-- If you modified the input size during training, please modify the `infer_shape` corresponding to VisionLAN in the `tools/export_model.py` file.
-
-After the conversion is successful, there are three files in the directory:
-```
-./inference/rec_r45_visionlan/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-For VisionLAN text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False
-```
-
-
-
-After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
-The result is as follows:
-```shell
-Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-1. Note that the MJSynth and SynthText datasets come from [VisionLAN repo](https://github.com/wangyuxin87/VisionLAN).
-2. We use the pre-trained model provided by the VisionLAN authors for finetune training. The dictionary for the pre-trained model is 'ppocr/utils/ic15_dict.txt'.
-
-## Citation
-
-```bibtex
-@inproceedings{wang2021two,
- title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network},
- author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong},
- booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
- pages={14194--14203},
- year={2021}
-}
-```
diff --git a/doc/doc_en/algorithm_rec_vitstr_en.md b/doc/doc_en/algorithm_rec_vitstr_en.md
deleted file mode 100644
index 7d5597f69c..0000000000
--- a/doc/doc_en/algorithm_rec_vitstr_en.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# ViTSTR
-
-- [1. Introduction](#1)
-- [2. Environment](#2)
-- [3. Model Training / Evaluation / Prediction](#3)
- - [3.1 Training](#3-1)
- - [3.2 Evaluation](#3-2)
- - [3.3 Prediction](#3-3)
-- [4. Inference and Deployment](#4)
- - [4.1 Python Inference](#4-1)
- - [4.2 C++ Inference](#4-2)
- - [4.3 Serving](#4-3)
- - [4.4 More](#4-4)
-- [5. FAQ](#5)
-
-
-## 1. Introduction
-
-Paper:
-> [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/abs/2105.08582)
-> Rowel Atienza
-> ICDAR, 2021
-
-Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
-
-|Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- |
-|ViTSTR|ViTSTR|[rec_vitstr_none_ce.yml](../../configs/rec/rec_vitstr_none_ce.yml)|79.82%|[trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar)|
-
-
-## 2. Environment
-Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
-
-
-
-## 3. Model Training / Evaluation / Prediction
-
-Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
-
-Training:
-
-Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
-
-```
-#Single GPU training (long training period, not recommended)
-python3 tools/train.py -c configs/rec/rec_vitstr_none_ce.yml
-
-#Multi GPU training, specify the gpu number through the --gpus parameter
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_vitstr_none_ce.yml
-```
-
-Evaluation:
-
-```
-# GPU evaluation
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
-```
-
-Prediction:
-
-```
-# The configuration file used for prediction must match the training
-python3 tools/infer_rec.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy
-```
-
-
-## 4. Inference and Deployment
-
-
-### 4.1 Python Inference
-First, the model saved during the ViTSTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar)) ), you can use the following command to convert:
-
-```
-python3 tools/export_model.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy Global.save_inference_dir=./inference/rec_vitstr
-```
-
-**Note:**
-- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
-- If you modified the input size during training, please modify the `infer_shape` corresponding to ViTSTR in the `tools/export_model.py` file.
-
-After the conversion is successful, there are three files in the directory:
-```
-/inference/rec_vitstr/
- ├── inference.pdiparams
- ├── inference.pdiparams.info
- └── inference.pdmodel
-```
-
-
-For ViTSTR text recognition model inference, the following commands can be executed:
-
-```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_vitstr/' --rec_algorithm='ViTSTR' --rec_image_shape='1,224,224' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt'
-```
-
-
-
-After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
-The result is as follows:
-```shell
-Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9998350143432617)
-```
-
-
-### 4.2 C++ Inference
-
-Not supported
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-1. In the `ViTSTR` paper, using pre-trained weights on ImageNet1k for initial training, we did not use pre-trained weights in training, and the final accuracy did not change or even improved.
-
-## Citation
-
-```bibtex
-@article{Atienza2021ViTSTR,
- title = {Vision Transformer for Fast and Efficient Scene Text Recognition},
- author = {Rowel Atienza},
- booktitle = {ICDAR},
- year = {2021},
- url = {https://arxiv.org/abs/2105.08582}
-}
-```
diff --git a/doc/doc_en/algorithm_sdmgr_en.md b/doc/doc_en/algorithm_sdmgr_en.md
deleted file mode 100644
index 7b3752223d..0000000000
--- a/doc/doc_en/algorithm_sdmgr_en.md
+++ /dev/null
@@ -1,72 +0,0 @@
-- [Key Information Extraction(KIE)](#key-information-extractionkie)
- - [1. Quick Use](#1-quick-use)
- - [2. Model Training](#2-model-training)
- - [3. Model Evaluation](#3-model-evaluation)
- - [4. Reference](#4-reference)
-
-# Key Information Extraction(KIE)
-
-This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR.
-
-[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
-
-## 1. Quick Use
-
-[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
-
-```shell
-wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
-```
-
-Download the pretrained model and predict the result:
-
-```shell
-cd PaddleOCR/
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
-python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=../wildreceipt/1.txt
-```
-
-The prediction result is saved as `./output/sdmgr_kie/predicts_kie.txt`, and the visualization results are saved in the folder`/output/sdmgr_kie/kie_results/`.
-
-The visualization results are shown in the figure below:
-
-
- 
- ', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' ', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344],
-...
-[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]]
-[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg
-[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246
-```
-
-**Note**:
-
-- TableMaster is relatively slow during inference, and it is recommended to use GPU for use.
-
-
-### 4.2 C++ Inference
-
-Since the post-processing is not written in CPP, the TableMaster does not support CPP inference.
-
-
-
-### 4.3 Serving
-
-Not supported
-
-
-### 4.4 More
-
-Not supported
-
-
-## 5. FAQ
-
-## Citation
-
-```bibtex
-@article{ye2021pingan,
- title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML},
- author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong},
- journal={arXiv preprint arXiv:2105.01848},
- year={2021}
-}
-```
diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md
deleted file mode 100644
index 00888fb04b..0000000000
--- a/doc/doc_en/angle_class_en.md
+++ /dev/null
@@ -1,153 +0,0 @@
-# Text Direction Classification
-
-- [1. Method Introduction](#method-introduction)
-- [2. Data Preparation](#data-preparation)
-- [3. Training](#training)
-- [4. Evaluation](#evaluation)
-- [5. Prediction](#prediction)
-
-
-
-## 1. Method Introduction
-The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system,
-The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support.
-
-Example of 0 and 180 degree data samples：
-
-
-
-
-## 2. Data Preparation
-
-Please organize the dataset as follows:
-
-The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
-
-```
-ln -sf /train_data/cls/dataset
-```
-
-please refer to the following to organize your data.
-
-- Training set
-
-First put the training images in the same folder (train_images), and use a txt file (cls_gt_train.txt) to store the image path and label.
-
-* Note: by default, the image path and image label are split with `\t`, if you use other methods to split, it will cause training error
-
-0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
-
-```
-" Image file name Image annotation "
-
-train/word_001.jpg 0
-train/word_002.jpg 180
-```
-
-The final training set should have the following file structure:
-
-```
-|-train_data
- |-cls
- |- cls_gt_train.txt
- |- train
- |- word_001.png
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-- Test set
-
-Similar to the training set, the test set also needs to be provided a folder
-containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
-
-```
-|-train_data
- |-cls
- |- cls_gt_test.txt
- |- test
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-## 3. Training
-Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file.
-
-PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
-
-Start training:
-
-```
-# Set PYTHONPATH path
-export PYTHONPATH=$PYTHONPATH:.
-# GPU training Support single card and multi-card training, specify the card number through --gpus.
-# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
-python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
-```
-
-- Data Augmentation
-
-PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
-
-The default perturbation methods are: cvtColor, blur, jitter, Gauss noise, random crop, perspective, color reverse, RandAugment.
-
-Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
-[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
-[randaugment.py](../../ppocr/data/imaug/randaugment.py)
-
-
-- Training
-
-PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 1000 iter. The following content will be saved during training:
-```bash
-├── best_accuracy.pdopt # Optimizer parameters for the best model
-├── best_accuracy.pdparams # Parameters of the best model
-├── best_accuracy.states # Metric info and epochs of the best model
-├── config.yml # Configuration file for this experiment
-├── latest.pdopt # Optimizer parameters for the latest model
-├── latest.pdparams # Parameters of the latest model
-├── latest.states # Metric info and epochs of the latest model
-└── train.log # Training log
-```
-
-If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
-
-**Note that the configuration file for prediction/evaluation must be consistent with the training.**
-
-
-## 4. Evaluation
-
-The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
-
-```
-export CUDA_VISIBLE_DEVICES=0
-# GPU evaluation, Global.checkpoints is the weight to be tested
-python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
-```
-
-## 5. Prediction
-
-* Training engine prediction
-
-Using the model trained by paddleocr, you can quickly get prediction through the following script.
-
-Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
-
-```
-# Predict English results
-python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
-```
-
-Input image:
-
-
-
-Get the prediction result of the input image:
-
-```
-infer_img: doc/imgs_words_en/word_10.png
- result: ('0', 0.9999995)
-```
diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md
deleted file mode 100755
index 62ac0258a9..0000000000
--- a/doc/doc_en/benchmark_en.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Benchmark
-
-This document gives the performance of the series models for Chinese and English recognition.
-
-## Test Data
-
-We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set.
-
-
-
-
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score |
-|:-:|:-:|:-:|:-:|:-:|:-:|
-| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 |
-| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 |
-| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 |
-
-Compares the time-consuming on CPU and T4 GPU (ms):
-
-| Model Name | CPU | T4 GPU |
-|:-:|:-:|:-:|
-| PP-OCRv2 | 330 | 111 |
-| PP-OCR mobile | 356 | 116|
-| PP-OCR server | 1056 | 200 |
-
-More indicators of PP-OCR series models can be referred to [PP-OCR Benchmark](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)
diff --git a/doc/doc_en/clone_en.md b/doc/doc_en/clone_en.md
deleted file mode 100644
index f9af283ce7..0000000000
--- a/doc/doc_en/clone_en.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Project Clone
-
-## 1. Clone PaddleOCR
-
-```bash
-# Recommend
-git clone https://github.com/PaddlePaddle/PaddleOCR
-
-# If you cannot pull successfully due to network problems, you can switch to the mirror hosted on Gitee:
-
-git clone https://gitee.com/paddlepaddle/PaddleOCR
-
-# Note: The mirror on Gitee may not keep in synchronization with the latest project on GitHub. There might be a delay of 3-5 days. Please try GitHub at first.
-```
-
-## 2. Install third-party libraries
-
-```bash
-cd PaddleOCR
-pip3 install -r requirements.txt
-```
diff --git a/doc/doc_en/code_and_doc.md b/doc/doc_en/code_and_doc.md
deleted file mode 100644
index d4a7cf4835..0000000000
--- a/doc/doc_en/code_and_doc.md
+++ /dev/null
@@ -1,349 +0,0 @@
- - Appendix
-
- This appendix contains python, document specifications and Pull Request process. Please follow the relevant contents
-
- - [Appendix 1：Python Code Specification](#Appendix1)
-
- - [Appendix 2：Document Specification](#Appendix2)
-
- - [Appendix 3：Pull Request Description](#Appendix3)
-
-
-
- ## Appendix 1：Python Code Specification
-
- The Python code of PaddleOCR follows [PEP8 Specification]( https://www.python.org/dev/peps/pep-0008/ ), some of the key concerns include the following
-
- - Space
-
- - Spaces should be added after commas, semicolons, colons, not before them
-
- ```python
- # true:
- print(x, y)
-
- # false:
- print(x , y)
- ```
-
- - When specifying a keyword parameter or default parameter value in a function, do not use spaces on both sides of it
-
- ```python
- # true:
- def complex(real, imag=0.0)
- # false:
- def complex(real, imag = 0.0)
- ```
-
- - comment
-
- - Inline comments: inline comments are indicated by the` # `sign. Two spaces should be left between code and` # `, and one space should be left between` # `and comments, for example
-
- ```python
- x = x + 1 # Compensate for border
- ```
-
- - Functions and methods: The definition of each function should include the following:
-
- - Function description: Utility, input and output of function
-
- - Args: Name and description of each parameter
- - Returns: The meaning and type of the return value
-
- ```python
- def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
- """Fetches rows from a Bigtable.
-
- Retrieves rows pertaining to the given keys from the Table instance
- represented by big_table. Silly things may happen if
- other_silly_variable is not None.
-
- Args:
- big_table: An open Bigtable Table instance.
- keys: A sequence of strings representing the key of each table row
- to fetch.
- other_silly_variable: Another optional variable, that has a much
- longer name than the other args, and which does nothing.
-
- Returns:
- A dict mapping keys to the corresponding table row data
- fetched. Each row is represented as a tuple of strings. For
- example:
-
- {'Serak': ('Rigel VII', 'Preparer'),
- 'Zim': ('Irk', 'Invader'),
- 'Lrrr': ('Omicron Persei 8', 'Emperor')}
-
- If a key from the keys argument is missing from the dictionary,
- then that row was not found in the table.
- """
- pass
- ```
-
-
-
- ## Appendix 2: Document Specification
-
- ### 2.1 Overall Description
-
- - Document Location: If you add new features to your original Markdown file, please **Do not re-create** a new file. If you don't know where to add it, you can first PR the code and then ask the official in commit.
-
- - New Markdown Document Name: Describe the content of the document in English, typically a combination of lowercase letters and underscores, such as `add_New_Algorithm.md`
-
- - New Markdown Document Format: Catalog - Body - FAQ
-
- > The directory generation method can use [this site](https://ecotrust-canada.github.io/markdown-toc/ ) Automatically extract directories after copying MD contents, and then add ` before each heading of the MD file
-
- - English and Chinese: Any changes or additions to the document need to be made in both Chinese and English documents.
-
- ### 2.2 Format Specification
-
- - Title format: The document title format follows the format of: Arabic decimal point combination-space-title (for example, `2.1 XXXX`, `2.XXXX`)
-
- - Code block: Displays code in code block format that needs to be run, describing the meaning of command parameters before the code block. for example:
-
- > Pipeline of detection + direction Classify + recognition: Vertical text can be recognized after set direction classifier parameters`--use_angle_cls true`.
- >
- > ```
- > paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
- > ```
-
- - Variable Rrferences: If code variables or command parameters are referenced in line, they need to be represented in line code, for example, above `--use_angle_cls true` with one space in front and one space in back
-
- - Uniform naming: e.g. PP-OCRv2, PP-OCR mobile, `paddleocr` whl package, PPOCRLabel, Paddle Lite, etc.
-
- - Supplementary notes: Supplementary notes by reference format `>`.
-
- - Picture: If a picture is added to the description document, specify the naming of the picture (describing its content) and add the picture under `doc/`.
-
- - Title: Capitalize the first letter of each word in the title.
-
-
-
- ## Appendix 3: Pull Request Description
-
- ### 3.1 PaddleOCR Branch Description
-
- PaddleOCR will maintain two branches in the future, one for each:
-
- - release/x.x family branch: stable release version branch, also the default branch. PaddleOCR releases a new release branch based on feature updates and adapts to the release version of Paddle. As versions iterate, more and more release/x.x family branches are maintained by default with the latest version of the release branch.
- - dygraph branch: For the development branch, adapts the dygraph version of the Paddle dynamic graph to primarily develop new functionality. If you need to redevelop, choose the dygraph branch. To ensure that the dygraph branch pulls out the release/x.x branch when needed, the code for the dygraph branch can only use the valid API in the latest release branch of Paddle. That is, if a new API has been developed in the Paddle dygraph branch but has not yet appeared in the release branch code, do not use it in Paddle OCR. In addition, performance optimization, parameter tuning, policy updates that do not involve API can be developed normally.
-
- The historical branch of PaddleOCR will no longer be maintained in the future. These branches will continue to be maintained, considering that some of you may still be using them:
-
- - Develop branch: This branch was used for the development and testing of static diagrams and is currently compatible with version >=1.7. If you have special needs, you can also use this branch to accommodate older versions of Paddle, but you won't update your code until you fix the bug.
-
- PaddleOCR welcomes you to actively contribute code to repo. Here are some basic processes for contributing code.
-
- ### 3.2 PaddleOCR Code Submission Process And Specification
-
- > If you are familiar with Git use, you can jump directly to [Some Conventions For Submitting Code in 3.2.10](#Some_conventions_for_submitting_code)
-
- #### 3.2.1 Create Your `Remote Repo`
-
- - In PaddleOCR [GitHub Home]( https://github.com/PaddlePaddle/PaddleOCR ) Click the `Fork` button in the upper left corner to create a `remote repo`in your personal directory, such as ` https://github.com/ {your_name}/PaddleOCR`.
-
- 
-
- - Clone `Remote repo`
-
- ```
- # pull code of develop branch
- git clone https://github.com/{your_name}/PaddleOCR.git -b dygraph
- cd PaddleOCR
- ```
-
- > Clone failures are mostly due to network reasons, try again later or configure the proxy
-
- #### 3.2.2 Login And Connect Using Token
-
- Start by viewing the information for the current `remote repo`.
-
- ```
- git remote -v
- # origin https://github.com/{your_name}/PaddleOCR.git (fetch)
- # origin https://github.com/{your_name}/PaddleOCR.git (push)
- ```
-
- Only the information of the clone `remote repo`, i.e. the PaddleOCR under your username, is available. Due to the change in Github's login method, you need to reconfigure the `remote repo` address by means of a Token. The token is generated as follows:
-
- 1. Find Personal Access Tokens: Click on your avatar in the upper right corner of the Github page and choose Settings --> Developer settings --> Personal access tokens,
-
- 2. Click Generate new token: Fill in the token name in Note, such as 'paddle'. In Select scopes, select repo (required), admin:repo_hook, delete_repo, etc. You can check them according to your needs. Then click Generate token to generate the token, and finally copy the generated token.
-
- Delete the original origin configuration
-
- ```
- git remote rm origin
- ```
-
- Change the remote branch to `https://oauth2:{token}@github.com/{your_name}/PaddleOCR.git`. For example, if the token value is 12345 and your user name is PPOCR, run the following command
-
- ```
- git remote add origin https://oauth2:12345@github.com/PPOCR/PaddleOCR.git
- ```
-
- This establishes a connection to our own `remote repo`. Next we create a remote host of the original PaddleOCR repo, named upstream.
-
- ```
- git remote add upstream https://github.com/PaddlePaddle/PaddleOCR.git
- ```
-
- Use `git remote -v` to view current `remote warehouse` information, output as follows, found to include two origin and two upstream of `remote repo` .
-
- ```
- origin https://github.com/{your_name}/PaddleOCR.git (fetch)
- origin https://github.com/{your_name}/PaddleOCR.git (push)
- upstream https://github.com/PaddlePaddle/PaddleOCR.git (fetch)
- upstream https://github.com/PaddlePaddle/PaddleOCR.git (push)
- ```
-
- This is mainly to keep the local repository up to date when subsequent pull request (PR) submissions are made.
-
- #### 3.2.3 Create Local Branch
-
- First get the latest code of upstream, then create a new_branch branch based on the dygraph of the upstream repo (upstream).
-
- ```
- git fetch upstream
- git checkout -b new_branch upstream/dygraph
- ```
-
- > If for a newly forked PaddleOCR project, the user's remote repo (origin) has the same branch updates as the upstream repository (upstream), you can also create a new local branch based on the default branch of the origin repo or a specified branch with the following command
- >
- > ```
- > # Create new_branch branch on user remote repo (origin) based on develop branch
- > git checkout -b new_branch origin/develop
- > # Create new_branch branch based on upstream remote repo develop branch
- > # If you need to create a new branch from upstream,
- > # you need to first use git fetch upstream to get upstream code
- > git checkout -b new_branch upstream/develop
- > ```
-
- The final switch to the new branch is displayed with the following output information.
-
- ```
- Branch new_branch set up to track remote branch develop from upstream.
- Switched to a new branch 'new_branch'
- ```
-
- After switching branches, file changes can be made on this branch
-
- #### 3.2.4 Use Pre-Commit Hook
-
- Paddle developers use the pre-commit tool to manage Git pre-submit hooks. It helps us format the source code (C++, Python) and automatically check for basic things (such as having only one EOL per file, not adding large files to Git) before committing it.
-
- The pre-commit test is part of the unit test in Travis-CI. PR that does not satisfy the hook cannot be submitted to PaddleOCR. Install it first and run it in the current directory：
-
- ```
- pip install pre-commit
- pre-commit install
- ```
-
- > 1. Paddle uses clang-format to adjust the C/C++ source code format. Make sure the `clang-format` version is above 3.8.
- >
- > 2. Yapf installed through pip install pre-commit is slightly different from conda install-c conda-forge pre-commit, and PaddleOCR developers use `pip install pre-commit`.
-
- #### 3.2.5 Modify And Submit Code
-
- If you make some changes on `README.Md ` on PaddleOCR, you can view the changed file through `git status`, and then add the changed file using `git add`。
-
- ```
- git status # View change files
- git add README.md
- pre-commit
- ```
-
- Repeat these steps until the pre-comit format check does not error. As shown below.
-
- 
-
- Use the following command to complete the submission.
-
- ```
- git commit -m "your commit info"
- ```
-
- #### 3.2.6 Keep Local Repo Up To Date
-
- Get the latest code for upstream and update the current branch. Here the upstream comes from section 2.2, `Connecting to a remote repo`.
-
- ```
- git fetch upstream
- # If you want to commit to another branch, you need to pull code from another branch of upstream, here is develop
- git pull upstream develop
- ```
-
- #### 3.2.7 Push To Remote Repo
-
- ```
- git push origin new_branch
- ```
-
- #### 3.2.7 Submit Pull Request
-
- Click the new pull request to select the local branch and the target branch, as shown in the following figure. In the description of PR, fill in the functions completed by the PR. Next, wait for review, and if you need to modify something, update the corresponding branch in origin with the steps above.
-
- 
-
- #### 3.2.8 Sign CLA Agreement And Pass Unit Tests
-
- - Signing the CLA When submitting a Pull Request to PaddlePaddle for the first time, you need to sign a CLA (Contributor License Agreement) agreement to ensure that your code can be incorporated as follows:
-
- 1. Please check the Check section in PR, find the license/cla, and click on the right detail to enter the CLA website
-
- 2. Click Sign in with GitHub to agree on the CLA website and when clicked, it will jump back to your Pull Request page
-
- #### 3.2.9 Delete Branch
-
- - Remove remote branch
-
- After PR is merged into the main repo, we can delete the branch of the remote repofrom the PR page.
- You can also use `git push origin:branch name` to delete remote branches, such as:
-
- ```
- git push origin :new_branch
- ```
-
-- Delete local branch
-
- ```
- # Switch to the development branch, otherwise the current branch cannot be deleted
- git checkout develop
-
- # Delete new_ Branch Branch
- git branch -D new_branch
- ```
-
-
-
- #### 3.2.10 Some Conventions For Submitting Code
-
- In order for official maintainers to better focus on the code itself when reviewing it, please follow the following conventions each time you submit your code:
-
- 1）Please ensure that the unit tests in Travis-CI pass smoothly. If not, indicate that there is a problem with the submitted code, and the official maintainer generally does not review it.
-
- 2）Before submitting a Pull Request.
-
- - Note the number of commits.
-
- Reason: If you only modify one file and submit more than a dozen commits, each commit will only make a few modifications, which can be very confusing to the reviewer. The reviewer needs to look at each commit individually to see what changes have been made, and does not exclude the fact that changes between commits overlap each other.
-
- Suggestion: Keep as few commits as possible each time you submit, and supplement your last commit with git commit --amend. For multiple commits that have been Push to a remote warehouse, you can refer to [squash commits after push](https://stackoverflow.com/questions/5667884/how-to-squash-commits-in-git-after-they-have-been-pushed ).
-
- - Note the name of each commit: it should reflect the content of the current commit, not be too arbitrary.
-
-
- 3） If you have solved a problem, add in the first comment box of the Pull Request:fix #issue_number，This will automatically close the corresponding Issue when the Pull Request is merged. Key words include:close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved,please choose the right vocabulary. Detailed reference [Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages).
-
- In addition, in response to the reviewer's comments, you are requested to abide by the following conventions:
-
- 1） Each review comment from an official maintainer would like a response, which would better enhance the contribution of the open source community.
-
- - If you agree to the review opinion and modify it accordingly, give a simple Done.
- - If you disagree with the review, please give your own reasons for refuting.
-
- 2）If there are many reviews:
-
- - Please give an overview of the changes.
- - Please reply with `start a review', not directly. The reason is that each reply sends an e-mail message, which can cause a mail disaster.
diff --git a/doc/doc_en/community_contribution_en.md b/doc/doc_en/community_contribution_en.md
deleted file mode 100644
index e2d24f6144..0000000000
--- a/doc/doc_en/community_contribution_en.md
+++ /dev/null
@@ -1,100 +0,0 @@
-# COMMUNITY CONTRIBUTION
-
-Thank you for your support and interest in PaddleOCR. The goal of PaddleOCR is to build a professional, harmonious and supportive open source community with developers. This document presents existing community contributions, explanations for various contributions, and new opportunities and processes to make the contribution process more efficient and clear.
-
-PaddleOCR wants to help any developer with a dream realize their vision and enjoy the joy of creating value through the power of AI.
-
----
-
-
-
-
-
-> The picture above shows PaddleOCR's current Contributor, updated regularly
-
-## 1. COMMUNITY CONTRIBUTION
-
-### 1.1 PaddleOCR BASED COMMUNITY PROJECT
-
-- 【The lastest】 [FastOCRLabel](https://gitee.com/BaoJianQiang/FastOCRLabel): Complete C# version annotation tool (@ [包建强](https://gitee.com/BaoJianQiang) )
-
-#### 1.1.1 UNIVERSAL TOOL
-
-- [DangoOCR offline version](https://github.com/PantsuDango/DangoOCR)：Universal desktop instant translation tool (@ [PantsuDango](https://github.com/PantsuDango))
-- [scr2txt](https://github.com/lstwzd/scr2txt)：Screenshot to Text tool (@ [lstwzd](https://github.com/lstwzd))
-- [AI Studio project](https://aistudio.baidu.com/aistudio/projectdetail/1054614?channelType=0&channel=0)：English video automatically generates subtitles( @ [叶月水狐](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/322052))
-
-#### 1.1.2 VERTICAL SCENE TOOLS
-
-- [id_card_ocr](https://github.com/baseli/id_card_ocr)：Identification of copy of ID card(@ [baseli](https://github.com/baseli))
-- [Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader): A data assistant that can read tables and pictures(@ [thunder95](https://github.com/thunder95]))
-
-#### 1.1.3 PRE AND POST PROCESSING
-
-- [paddleOCRCorrectOutputs](https://github.com/yuranusduke/paddleOCRCorrectOutputs)：Get the key-value of OCR recognition result (@ [yuranusduke](https://github.com/yuranusduke))
-
-### 1.2 NEW FEATURES FOR PaddleOCR
-
-- Thanks [authorfu](https://github.com/authorfu) for contributing Android([#340](https://github.com/PaddlePaddle/PaddleOCR/pull/340)) and [xiadeye](https://github.com/xiadeye) for contributing IOS demo code([#325](https://github.com/PaddlePaddle/PaddleOCR/pull/325)).
-- Thanks [tangmq](https://gitee.com/tangmq) for adding docker deployment service to PaddleOCR to support quick release of callable restful API services([#507](https://github.com/PaddlePaddle/PaddleOCR/pull/507)).
-- Thanks [lijinhan](https://github.com/lijinhan) for adding Java springboot to PaddleOCR and call OCR hubserving interface to complete the use of OCR service deployment([#1027](https://github.com/PaddlePaddle/PaddleOCR/pull/1027)).
-- Thanks [Evezerest](https://github.com/Evezerest), [ninetailskim](https://github.com/ninetailskim), [edencfc](https://github.com/edencfc), [BeyondYourself](https://github.com/BeyondYourself), [1084667371](https://github.com/1084667371) for contributing complete code of [PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/README_ch.md).
-
-### 1.3 CODE AND DOCUMENT OPTIMIZATION
-
-- Thanks [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) for contributing new visualization methods and adding .gitgnore, handling the problem of manually setting the PYTHONPATH environment variable([#210](https://github.com/PaddlePaddle/PaddleOCR/pull/210)).
-- Thanks [lyl120117](https://github.com/lyl120117) for contributing code to print network structure([#304](https://github.com/PaddlePaddle/PaddleOCR/pull/304)).
-- Thanks [BeyondYourself](https://github.com/BeyondYourself) for making a lot of great suggestions for PaddleOCR and simplifying some code styles of paddleocr([so many commits)](https://github.com/PaddlePaddle/PaddleOCR/commits?author=BeyondYourself).
-- Thanks [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing modifing English documents.
-
-### 1.4 MULTILINGUAL CORPUS
-
-- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing handwritting Chinese OCR dataset([#321](https://github.com/PaddlePaddle/PaddleOCR/pull/321)).
-- Thanks [Mejans](https://github.com/Mejans) for contributing dictionary and corpus of the new language Occitan to PaddleOCR([#954](https://github.com/PaddlePaddle/PaddleOCR/pull/954)).
-
-## 2. CONTRIBUTION ILLUSTRATING
-
-### 2.1 NEW FUNCTION CLASS
-
-PaddleOCR welcomes community contributions to various services, deployment examples and software applications with paddleOCR as the core. Certified community contributions will be added to the above community contribution table to increase exposure for the majority of developers, which is also the glory of PaddleOCR, including:
-
-- Project form: the project code certified by the official community shall have good specifications and structure, and shall be equipped with a detailed README.md, which describes how to use the project. Through add a line 'paddleocr' to the requirements.txt, which can be automatically included in the usedby of paddleocr.
-
-- Integration method: if it is an update to the existing PaddleOCR tool, it will be integrated into the main repo. If a new function is expanded for paddleocr, please contact the official personnel first to confirm whether the project is integrated into the master repo, *even if the new function is not integrated into the master repo, we will also increase the exposure of your personal project in the way of community contribution.*
-
-
-### 2.2 CODE OPTIMIZATION
-
-If you encounter code bugs and unexpected functions when using PaddleOCR, you can contribute your modifications to PaddleOCR, including:
-
-- Python code specifications are available for reference [Appendix 1:Python code specifications](./code_and_doc.md/#Appendix1).
-
-- Before submitting the code, please confirm again and again that no new bugs will be introduced, and describe the optimization points in the PR. If the PR solves an issue, please connect to the issue in the PR. All PR shall comply with the requirements in Appendix [3.2.10 Some conventions for submitting code.](./code_and_doc.md/#Some conventions for submitting code)
-
-- Please refer to the below before submitting. If you are not familiar with the git submission process, you can also refer to Section 3.2 of [Appendix 3: description of Pull Request](./code_and_doc.md/#Appendix3).If you are not familiar with the git submission process, you can also refer to Section 3.2 of Appendix 3.
-
-**Finally, please add the label Third Party in the title of PR and @ Everest in the description , PR with this label will be treated with high priority`[third-part]`.**
-
-### 2.3 DOCUMENT OPTIMIZATION
-
-If you encounter problems such as unclear document description, missing description and invalid link when using PaddleOCR, you can contribute your modifications to PaddleOCR. For document writing specifications, please refer to [Appendix 2: document specifications](./code_and_doc.md/#Appendix2). **Finally, please add the label Third Party in the title of PR and @ Everest in the description , PR with this label will be treated with high priority`[third-party].**
-
-## 3. MORE CONTRIBUTION OPPORTUNITIES
-
-We encourage developers to use PaddleOCR to realize their ideas. At the same time, we also list some valuable development directions after analysis, which are collected in the regular season of community projects as a whole.
-
-## 4. CONTACT US
-
-We very much welcome developers to contact us before they intend to contribute code, documents, corpus and other contents to PaddleOCR, which can greatly reduce the communication cost in the PR process. At the same time, if you find some ideas difficult to realize personally, we can also recruit like-minded developers for the project in the form of SIG. Projects funded through SIG channels will receive deep R & D support and operational resources (such as official account publicity, live broadcast lessons, etc.).
-
-Our recommended contribution process is:
-
-- By adding the `[Third Party]` mark in the topic of GitHub issue, explain the problems encountered (and the ideas to solve) or the functions to be expanded, and wait for the reply of the person on duty. For example, ` [Third Party] contributes IOS examples to PaddleOCR`.
-- After communicating with us and confirming that the technical scheme or bugs and optimization points are correct, add functions or modify them accordingly, and the codes and documents shall comply with relevant specifications.
-- PR links to the above issue and waits for review.
-
-## 5. THANKS AND FOLLOW-UP
-
- - After the code is combined, the information will be updated in the first section of this document. The default link is GitHub name and home page. If you need to change the home page, you can also contact us.
- - New important function classes will be advertised in the user group and enjoy the honor of the open source community.
- - **If you have a PaddleOCR based project that does not appear in the above list, follow `4. CONTACT US` .**
diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md
deleted file mode 100644
index b7c828f071..0000000000
--- a/doc/doc_en/config_en.md
+++ /dev/null
@@ -1,248 +0,0 @@
-# Configuration
-
-- [1. Optional Parameter List](#1-optional-parameter-list)
-- [2. Introduction to Global Parameters of Configuration File](#2-introduction-to-global-parameters-of-configuration-file)
-- [3. Multilingual Config File Generation](#3-multilingual-config-file-generation)
-
-
-
-## 1. Optional Parameter List
-
-The following list can be viewed through `--help`
-
-| FLAG | Supported script | Use | Defaults | Note |
-| :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: |
-| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
-| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false |
-
-
-
-## 2. Introduction to Global Parameters of Configuration File
-
-Take rec_chinese_lite_train_v2.0.yml as an example
-### Global
-
-| Parameter | Use | Defaults | Note |
-| :----------------------: | :---------------------: | :--------------: | :--------------------: |
-| use_gpu | Set using GPU or not | true | \ |
-| epoch_num | Maximum training epoch number | 500 | \ |
-| log_smooth_window | Log queue length, the median value in the queue each time will be printed | 20 | \ |
-| print_batch_step | Set print log interval | 10 | \ |
-| save_model_dir | Set model save path | output/{算法名称} | \ |
-| save_epoch_step | Set model save interval | 3 | \ |
-| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | running evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
-| cal_metric_during_train | Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated | true | \ |
-| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ |
-| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
-| checkpoints | set model parameter path | None | Used to load parameters after interruption to continue training|
-| use_visualdl | Set whether to enable visualdl for visual log display | False | [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
-| use_wandb | Set whether to enable W&B for visual log display | False | [Documentation](https://docs.wandb.ai/)
-| infer_img | Set inference image path or folder path | ./infer_img | \||
-| character_dict_path | Set dictionary path | ./ppocr/utils/ppocr_keys_v1.txt | If the character_dict_path is None, model can only recognize number and lower letters |
-| max_text_length | Set the maximum length of text | 25 | \ |
-| use_space_char | Set whether to recognize spaces | True | \| |
-| label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in angle classifier model |
-| save_res_path | Set the save address of the test model results | ./output/det_db/predicts_db.txt | Only valid in the text detection model |
-
-### Optimizer ([ppocr/optimizer](../../ppocr/optimizer))
-
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | Optimizer class name | Adam | Currently supports`Momentum`,`Adam`,`RMSProp`, see [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) |
-| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ |
-| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ |
-| clip_norm | The maximum norm value | - | \ |
-| **lr** | Set the learning rate decay method | - | \ |
-| name | Learning rate decay class name | Cosine | Currently supports`Linear`,`Cosine`,`Step`,`Piecewise`, see[ppocr/optimizer/learning_rate.py](../../ppocr/optimizer/learning_rate.py) |
-| learning_rate | Set the base learning rate | 0.001 | \ |
-| **regularizer** | Set network regularization method | - | \ |
-| name | Regularizer class name | L2 | Currently support`L1`,`L2`, see[ppocr/optimizer/regularizer.py](../../ppocr/optimizer/regularizer.py) |
-| factor | Regularizer coefficient | 0.00001 | \ |
-
-
-### Architecture ([ppocr/modeling](../../ppocr/modeling))
-In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck and Head
-
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
-| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview_en.md) for the support list |
-| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transforms) for details |
-| name | Transformation class name | TPS | Currently supports `TPS` |
-| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
-| loc_lr | Localization network learning rate | 0.1 | \ |
-| model_name | Localization network size | small | Currently support`small`,`large` |
-| **Backbone** | Set the network backbone class name | - | see [ppocr/modeling/backbones](../../ppocr/modeling/backbones) |
-| name | backbone class name | ResNet | Currently support`MobileNetV3`,`ResNet` |
-| layers | resnet layers | 34 | Currently support18,34,50,101,152,200 |
-| model_name | MobileNetV3 network size | small | Currently support`small`,`large` |
-| **Neck** | Set network neck | - | see[ppocr/modeling/necks](../../ppocr/modeling/necks) |
-| name | neck class name | SequenceEncoder | Currently support`SequenceEncoder`,`DBFPN` |
-| encoder_type | SequenceEncoder encoder type | rnn | Currently support`reshape`,`fc`,`rnn` |
-| hidden_size | rnn number of internal units | 48 | \ |
-| out_channels | Number of DBFPN output channels | 256 | \ |
-| **Head** | Set the network head | - | see[ppocr/modeling/heads](../../ppocr/modeling/heads) |
-| name | head class name | CTCHead | Currently support`CTCHead`,`DBHead`,`ClsHead` |
-| fc_decay | CTCHead regularization coefficient | 0.0004 | \ |
-| k | DBHead binarization coefficient | 50 | \ |
-| class_dim | ClsHead output category number | 2 | \ |
-
-
-### Loss ([ppocr/losses](../../ppocr/losses))
-
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | loss class name | CTCLoss | Currently support`CTCLoss`,`DBLoss`,`ClsLoss` |
-| balance_loss | Whether to balance the number of positive and negative samples in DBLossloss (using OHEM) | True | \ |
-| ohem_ratio | The negative and positive sample ratio of OHEM in DBLossloss | 3 | \ |
-| main_loss_type | The loss used by shrink_map in DBLossloss | DiceLoss | Currently support`DiceLoss`,`BCELoss` |
-| alpha | The coefficient of shrink_map_loss in DBLossloss | 5 | \ |
-| beta | The coefficient of threshold_map_loss in DBLossloss | 10 | \ |
-
-### PostProcess ([ppocr/postprocess](../../ppocr/postprocess))
-
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | Post-processing class name | CTCLabelDecode | Currently support`CTCLoss`,`AttnLabelDecode`,`DBPostProcess`,`ClsPostProcess` |
-| thresh | The threshold for binarization of the segmentation map in DBPostProcess | 0.3 | \ |
-| box_thresh | The threshold for filtering output boxes in DBPostProcess. Boxes below this threshold will not be output | 0.7 | \ |
-| max_candidates | The maximum number of text boxes output in DBPostProcess | 1000 | |
-| unclip_ratio | The unclip ratio of the text box in DBPostProcess | 2.0 | \ |
-
-### Metric ([ppocr/metrics](../../ppocr/metrics))
-
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| name | Metric method name | CTCLabelDecode | Currently support`DetMetric`,`RecMetric`,`ClsMetric` |
-| main_indicator | Main indicators, used to select the best model | acc | For the detection method is hmean, the recognition and classification method is acc |
-
-### Dataset ([ppocr/data](../../ppocr/data))
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| **dataset** | Return one sample per iteration | - | - |
-| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDataSet` |
-| data_dir | Image folder path | ./train_data | \ |
-| label_file_list | Groundtruth file path | ["./train_data/train_list.txt"] | This parameter is not required when dataset is LMDBDataSet |
-| ratio_list | Ratio of data set | [1.0] | If there are two train_lists in label_file_list and ratio_list is [0.4,0.6], 40% will be sampled from train_list1, and 60% will be sampled from train_list2 to combine the entire dataset |
-| transforms | List of methods to transform images and labels | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | see[ppocr/data/imaug](../../ppocr/data/imaug) |
-| **loader** | dataloader related | - | |
-| shuffle | Does each epoch disrupt the order of the data set | True | \ |
-| batch_size_per_card | Single card batch size during training | 256 | \ |
-| drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ |
-| num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ |
-
-### Weights & Biases ([W&B](../../ppocr/utils/loggers/wandb_logger.py))
-| Parameter | Use | Defaults | Note |
-| :---------------------: | :---------------------: | :--------------: | :--------------------: |
-| project | Project to which the run is to be logged | uncategorized | \
-| name | Alias/Name of the run | Randomly generated by wandb | \
-| id | ID of the run | Randomly generated by wandb | \
-| entity | User or team to which the run is being logged | The logged in user | \
-| save_dir | local directory in which all the models and other data is saved | wandb | \
-| config | model configuration | None | \
-
-
-
-
-## 3. Multilingual Config File Generation
-
-PaddleOCR currently supports recognition for 80 languages (besides Chinese). A multi-language configuration file template is
-provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
-
-There are two ways to create the required configuration file:
-
-1. Automatically generated by script
-
-Script [generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) can help you generate configuration files for multi-language models.
-
-- Take Italian as an example, if your data is prepared in the following format:
- ```
- |-train_data
- |- it_train.txt # train_set label
- |- it_val.txt # val_set label
- |- data
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
- ```
-
- You can use the default parameters to generate a configuration file:
-
- ```bash
- # The code needs to be run in the specified directory
- cd PaddleOCR/configs/rec/multi_language/
- # Set the configuration file of the language to be generated through the -l or --language parameter.
- # This command will write the default parameters into the configuration file
- python3 generate_multi_language_configs.py -l it
- ```
-
-- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:
-
- ```bash
- # -l or --language field is required
- # --train to modify the training set
- # --val to modify the validation set
- # --data_dir to modify the data set directory
- # --dict to modify the dict path
- # -o to modify the corresponding default parameters
- cd PaddleOCR/configs/rec/multi_language/
- python3 generate_multi_language_configs.py -l it \ # language
- --train {path/of/train_label.txt} \ # path of train_label
- --val {path/of/val_label.txt} \ # path of val_label
- --data_dir {train_data/path} \ # root directory of training data
- --dict {path/of/dict} \ # path of dict
- -o Global.use_gpu=False # whether to use gpu
- ...
-
- ```
-Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.
-
-2. Manually modify the configuration file
-
- You can also manually modify the following fields in the template:
-
- ```
- Global:
- use_gpu: True
- epoch_num: 500
- ...
- character_dict_path: {path/of/dict} # path of dict
-
- Train:
- dataset:
- name: SimpleDataSet
- data_dir: train_data/ # root directory of training data
- label_file_list: ["./train_data/train_list.txt"] # train label path
- ...
-
- Eval:
- dataset:
- name: SimpleDataSet
- data_dir: train_data/ # root directory of val data
- label_file_list: ["./train_data/val_list.txt"] # val label path
- ...
-
- ```
-
-
-Currently, the multi-language algorithms supported by PaddleOCR are:
-
-| Configuration file | Algorithm name | backbone | trans | seq | pred | language |
-| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
-| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional |
-| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) |
-| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French |
-| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German |
-| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese |
-| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean |
-| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Latin |
-| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | arabic |
-| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | cyrillic |
-| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | devanagari |
-
-For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
-
-The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
-* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
-* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
diff --git a/doc/doc_en/customize_en.md b/doc/doc_en/customize_en.md
deleted file mode 100644
index 80b8302238..0000000000
--- a/doc/doc_en/customize_en.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# HOW TO MAKE YOUR OWN LIGHTWEIGHT OCR MODEL?
-
-The process of making a customized ultra-lightweight OCR models can be divided into three steps: training text detection model, training text recognition model, and concatenate the predictions from previous steps.
-
-## STEP1: TRAIN TEXT DETECTION MODEL
-
-PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
-```
-python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
-```
-For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
-
-## STEP2: TRAIN TEXT RECOGNITION MODEL
-
-PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
-```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
-```
-For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)
-
-## STEP3: CONCATENATE PREDICTIONS
-
-PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
-
-When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recognition model. The visualized results are saved to the `./inference_results` folder by default.
-
-```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
-```
-For more details about text detection and recognition concatenation, please refer to the document [Inference](./inference_en.md)
diff --git a/doc/doc_en/data_annotation_en.md b/doc/doc_en/data_annotation_en.md
deleted file mode 100644
index 71c8f4f61c..0000000000
--- a/doc/doc_en/data_annotation_en.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# DATA ANNOTATION TOOLS
-There are the commonly used data annotation tools, which will be continuously updated. Welcome to contribute tools~
-
-### 1. labelImg
-- Tool description: Rectangular label
-- Tool address: https://github.com/tzutalin/labelImg
-- Sketch diagram:
-
-
-### 2. roLabelImg
-- Tool description: Label tool rewritten based on labelImg, supporting rotating rectangular label
-- Tool address: https://github.com/cgvict/roLabelImg
-- Sketch diagram:
-
-
-### 3. labelme
-- Tool description: Support four points, polygons, circles and other labels
-- Tool address: https://github.com/wkentaro/labelme
-- Sketch diagram:
-
diff --git a/doc/doc_en/data_synthesis_en.md b/doc/doc_en/data_synthesis_en.md
deleted file mode 100644
index f05a793cf5..0000000000
--- a/doc/doc_en/data_synthesis_en.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# DATA SYNTHESIS TOOLS
-
-In addition to open source data, users can also use synthesis tools to synthesize data.
-There are the commonly used data synthesis tools, which will be continuously updated. Welcome to contribute tools~
-
-* [Text_renderer](https://github.com/Sanster/text_renderer)
-* [SynthText](https://github.com/ankush-me/SynthText)
-* [SynthText_Chinese_version](https://github.com/JarveeLee/SynthText_Chinese_version)
-* [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator)
-* [SynthText3D](https://github.com/MhLiao/SynthText3D)
-* [UnrealText](https://github.com/Jyouhou/UnrealText/)
-* [SynthTIGER](https://github.com/clovaai/synthtiger)
diff --git a/doc/doc_en/dataset/datasets_en.md b/doc/doc_en/dataset/datasets_en.md
deleted file mode 100644
index 2a7c9c5df2..0000000000
--- a/doc/doc_en/dataset/datasets_en.md
+++ /dev/null
@@ -1,61 +0,0 @@
-## DATASET
-This is a collection of commonly used Chinese datasets, which is being updated continuously. You are welcome to contribute to this list～
-- [ICDAR2019-LSVT](#ICDAR2019-LSVT)
-- [ICDAR2017-RCTW-17](#ICDAR2017-RCTW-17)
-- [Chinese Street View Text Recognition](#中文街景文字识别)
-- [Chinese Document Text Recognition](#中文文档文字识别)
-- [ICDAR2019-ArT](#ICDAR2019-ArT)
-
-In addition to opensource data, users can also use synthesis tools to synthesize data themselves. Current available synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator), etc.
-
-
-#### 1. ICDAR2019-LSVT
-- **Data sources**：https://ai.baidu.com/broad/introduction?dataset=lsvt
-- **Introduction**： A total of 45w Chinese street view images, including 5w (2w test + 3w training) fully labeled data (text coordinates + text content), 40w weakly labeled data (text content only), as shown in the following figure:
- 
-
- (a) Fully labeled data
-
- 
-
- (b) Weakly labeled data
-- **Download link**：https://ai.baidu.com/broad/download?dataset=lsvt
-
-
-#### 2. ICDAR2017-RCTW-17
-- **Data sources**：https://rctw.vlrlab.net/
-- **Introduction**：It contains 12000 + images, most of them are collected in the wild through mobile camera. Some are screenshots. These images show a variety of scenes, including street views, posters, menus, indoor scenes and screenshots of mobile applications.
- 
-- **Download link**：https://rctw.vlrlab.net/dataset/
-
-
-#### 3. Chinese Street View Text Recognition
-- **Data sources**：https://aistudio.baidu.com/aistudio/competition/detail/8
-- **Introduction**：A total of 290000 pictures are included, of which 210000 are used as training sets (with labels) and 80000 are used as test sets (without labels). The dataset is collected from the Chinese street view, and is formed by by cutting out the text line area (such as shop signs, landmarks, etc.) in the street view picture. All the images are preprocessed: by using affine transform, the text area is proportionally mapped to a picture with a height of 48 pixels, as shown in the figure:
-
- 
- (a) Label: 魅派集成吊顶
- 
- (b) Label: 母婴用品连锁
-- **Download link**
-https://aistudio.baidu.com/aistudio/datasetdetail/8429
-
-
-#### 4. Chinese Document Text Recognition
-- **Data sources**：https://github.com/YCG09/chinese_ocr
-- **Introduction**：
- - A total of 3.64 million pictures are divided into training set and validation set according to 99:1.
- - Using Chinese corpus (news + classical Chinese), the data is randomly generated through changes in font, size, grayscale, blur, perspective, stretching, etc.
- - 5990 characters including Chinese characters, English letters, numbers and punctuation（Characters set: https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt ）
- - Each sample is fixed with 10 characters, and the characters are randomly intercepted from the sentences in the corpus
- - Image resolution is 280x32
- 
- 
-- **Download link**：https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (Password: lu7m)
-
-
-#### 5、ICDAR2019-ArT
-- **Data source**：https://ai.baidu.com/broad/introduction?dataset=art
-- **Introduction**：It includes 10166 images, 5603 in training sets and 4563 in test sets. It is composed of three parts: total text, scut-ctw1500 and Baidu curved scene text, including text with various shapes such as horizontal, multi-directional and curved.
- 
-- **Download link**：https://ai.baidu.com/broad/download?dataset=art
diff --git a/doc/doc_en/dataset/handwritten_datasets_en.md b/doc/doc_en/dataset/handwritten_datasets_en.md
deleted file mode 100644
index 2059549601..0000000000
--- a/doc/doc_en/dataset/handwritten_datasets_en.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Handwritten OCR dataset
-Here we have sorted out the commonly used handwritten OCR dataset datasets, which are being updated continuously. We welcome you to contribute datasets ~
-- [Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset](#Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset)
-- [NIST handwritten single character dataset - English](#NIST handwritten single character dataset - English)
-
-
-## Institute of automation, Chinese Academy of Sciences - handwritten Chinese dataset
-- **Data source**:http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
-- **Data introduction**:
- * It includes online and offline handwritten data,`HWDB1.0~1.2` has totally 3895135 handwritten single character samples, which belong to 7356 categories (7185 Chinese characters and 171 English letters, numbers and symbols);`HWDB2.0~2.2` has totally 5091 pages of images, which are divided into 52230 text lines and 1349414 words. All text and text samples are stored as grayscale images. Some sample words are shown below.
-
- 
-
-- **Download address**:http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
-- **使用建议**:Data for single character, white background, can form a large number of text lines for training. White background can be processed into transparent state, which is convenient to add various backgrounds. For the case of semantic needs, it is suggested to extract single character from real corpus to form text lines.
-
-
-
-## NIST handwritten single character dataset - English(NIST Handprinted Forms and Characters Database)
-
-- **Data source**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
-
-- **Data introduction**: NIST19 dataset is suitable for handwritten document and character recognition model training. It is extracted from the handwritten sample form of 3600 authors and contains 810000 character images in total. Nine of them are shown below.
-
- 
-
-
-- **Download address**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
diff --git a/doc/doc_en/dataset/kie_datasets_en.md b/doc/doc_en/dataset/kie_datasets_en.md
deleted file mode 100644
index ff839097c5..0000000000
--- a/doc/doc_en/dataset/kie_datasets_en.md
+++ /dev/null
@@ -1,49 +0,0 @@
-## Key Information Extraction dataset
-
-Here are the common datasets key information extraction, which are being updated continuously. Welcome to contribute datasets.
-
-- [FUNSD dataset](#funsd)
-- [XFUND dataset](#xfund)
-- [wildreceipt dataset](#wildreceipt-dataset)
-
-
-#### 1. FUNSD dataset
-- **Data source**: https://guillaumejaume.github.io/FUNSD/
-- **Data Introduction**: The FUNSD dataset is a dataset for form comprehension. It contains 199 real, fully annotated scanned images, including market reports, advertisements, and academic reports, etc., and is divided into 149 training set and 50 test set. The FUNSD dataset is suitable for many types of DocVQA tasks, such as field-level entity classification, field-level entity connection, etc. Part of the image and the annotation box visualization are shown below:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. Then download the PaddleOCR format annotation file from the table above.
-
-PaddleOCR also provides a data format conversion script, which can convert the official website label to the PaddleOCR format. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
-```
-# Convert the label file downloaded from the official website to train_icdar2015_label.txt
-python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/" \
- --input_path="/path/to/ch4_training_localization_transcription_gt" \
- --output_label="/path/to/train_icdar2015_label.txt"
-```
-
-After decompressing the data set and downloading the annotation file, PaddleOCR/train_data/ has two folders and two files, which are:
-```
-/PaddleOCR/train_data/icdar2015/text_localization/
- └─ icdar_c4_train_imgs/ Training data of icdar dataset
- └─ ch4_test_images/ Testing data of icdar dataset
- └─ train_icdar2015_label.txt Training annotation of icdar dataset
- └─ test_icdar2015_label.txt Test annotation of icdar dataset
-```
-
-
-## 2. Text recognition
-
-### 2.1 PaddleOCR text recognition format annotation
-
-The text recognition algorithm in PaddleOCR supports two data formats:
- - `lmdb` is used to train data sets stored in lmdb format, use [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) to load;
- - `common dataset` is used to train data sets stored in text files, use [simple_dataset.py](../../../ppocr/data/simple_dataset.py) to load.
-
-
-If you want to use your own data for training, please refer to the following to organize your data.
-
-- Training set
-
-It is recommended to put the training images in the same folder, and use a txt file (rec_gt_train.txt) to store the image path and label. The contents of the txt file are as follows:
-
-* Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error
-
-```
-" Image file name Image annotation "
-
-train_data/rec/train/word_001.jpg 简单可依赖
-train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
-...
-```
-
-The final training set should have the following file structure:
-
-```
-|-train_data
- |-rec
- |- rec_gt_train.txt
- |- train
- |- word_001.png
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-- Test set
-
-Similar to the training set, the test set also needs to be provided a folder containing all images (test) and a rec_gt_test.txt. The structure of the test set is as follows:
-
-```
-|-train_data
- |-rec
- |-ic15_data
- |- rec_gt_test.txt
- |- test
- |- word_001.jpg
- |- word_002.jpg
- |- word_003.jpg
- | ...
-```
-
-### 2.2 Public dataset
-| dataset | Image download link | PaddleOCR format annotation download link |
-|---|---|---|
-| en benchmark(MJ, SJ, IIIT, SVT, IC03, IC13, IC15, SVTP, and CUTE.) | [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | LMDB format, which can be loaded directly with [lmdb_dataset.py](../../../ppocr/data/lmdb_dataset.py) |
-|ICDAR 2015| http://rrc.cvc.uab.es/?ch=4&com=downloads | [train](https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt)/ [test](https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt) |
-| Multilingual datasets |[Baidu network disk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) Extraction code: frgi
[google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) | Included in the downloaded image zip |
-
-#### 2.1 ICDAR 2015
-
-The ICDAR 2015 dataset can be downloaded from the link in the table above for quick validation. The lmdb format dataset required by en benchmark can also be downloaded from the table above.
-
-Then download the PaddleOCR format annotation file from the table above.
-
-PaddleOCR also provides a data format conversion script, which can convert the ICDAR official website label to the data format supported by PaddleOCR. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
-
-```
-# Convert the label file downloaded from the official website to rec_gt_label.txt
-python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
-```
-
-The data format is as follows, (a) is the original picture, (b) is the Ground Truth text file corresponding to each picture:
-
-
-
-## 3. Data storage path
-
-The default storage path for PaddleOCR training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
-
-```
-# linux and mac os
-ln -sf /train_data/dataset
-# windows
-mklink /d /train_data/dataset
-```
diff --git a/doc/doc_en/dataset/table_datasets_en.md b/doc/doc_en/dataset/table_datasets_en.md
deleted file mode 100644
index 70ca830979..0000000000
--- a/doc/doc_en/dataset/table_datasets_en.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Table Recognition Datasets
-
-- [Dataset Summary](#dataset-summary)
-- [1. PubTabNet](#1-pubtabnet)
-- [2. TAL Table Recognition Competition Dataset](#2-tal-table-recognition-competition-dataset)
-- [3. WTW Chinese scene table dataset](#3-wtw-chinese-scene-table-dataset)
-
-Here are the commonly used table recognition datasets, which are being updated continuously. Welcome to contribute datasets~
-
-## Dataset Summary
-
-| dataset | Image download link | PPOCR format annotation download link |
-|---|---|---|
-| PubTabNet |https://github.com/ibm-aur-nlp/PubTabNet| jsonl format, which can be loaded directly with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py) |
-| TAL Table Recognition Competition Dataset |https://ai.100tal.com/dataset| jsonl format, which can be loaded directly with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py) |
-| WTW Chinese scene table dataset |https://github.com/wangwen-whu/WTW-Dataset| Conversion is required to load with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)|
-
-## 1. PubTabNet
-- **Data Introduction**：The training set of the PubTabNet dataset contains 500,000 images and the validation set contains 9000 images. Part of the image visualization is shown below.
-
-
-
-
-
-
-
-
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-
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-
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-
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- 
- 
-
diff --git a/doc/doc_en/ppocr_introduction_en.md b/doc/doc_en/ppocr_introduction_en.md
deleted file mode 100644
index 16a078dc02..0000000000
--- a/doc/doc_en/ppocr_introduction_en.md
+++ /dev/null
@@ -1,128 +0,0 @@
-English | [简体中文](../doc_ch/ppocr_introduction.md)
-
-# PP-OCR
-
-- [1. Introduction](#1)
-- [2. Features](#2)
-- [3. Benchmark](#3)
-- [4. Visualization](#4)
-- [5. Tutorial](#5)
- - [5.1 Quick start](#51)
- - [5.2 Model training / compression / deployment](#52)
-- [6. Model zoo](#6)
-
-
-
-## 1. Introduction
-
-PP-OCR is a self-developed practical ultra-lightweight OCR system, which is slimed and optimized based on the reimplemented [academic algorithms](algorithm_en.md), considering the balance between **accuracy** and **speed**.
-
-#### PP-OCR
-PP-OCR is a two-stage OCR system, in which the text detection algorithm is [DB](algorithm_det_db_en.md), and the text recognition algorithm is [CRNN](algorithm_rec_crnn_en.md). Besides, a [text direction classifier](angle_class_en.md) is added between the detection and recognition modules to deal with text in different directions.
-
-PP-OCR pipeline is as follows:
-
-
-
-
-
-
-PP-OCRv3 Chinese model
-
-
-
-
-PP-OCRv3 English model
-
-
-
-
-
-
-PP-OCRv3 Multilingual model
-
-
-
-
-
-
-
-## 5. Tutorial
-
-
-### 5.1 Quick start
-
-- You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
-- Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
-- One line of code quick use: [Quick Start](./quickstart_en.md)
-
-
-### 5.2 Model training / compression / deployment
-
-For more tutorials, including model training, model compression, deployment, etc., please refer to [tutorials](../../README.md#Tutorials)。
-
-
-## 6. Model zoo
-
-## PP-OCR Series Model List（Update on 2022.04.28）
-
-| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
-| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| Chinese and English ultra-lightweight PP-OCRv4 model（15.8M） | ch_PP-OCRv4_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_train.tar) |
-| Chinese and English ultra-lightweight PP-OCRv3 model（16.2M） | ch_PP-OCRv3_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
-| English ultra-lightweight PP-OCRv3 model（13.4M） | en_PP-OCRv3_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
-| Chinese and English ultra-lightweight PP-OCRv2 model（11.6M） | ch_PP-OCRv2_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
-| Chinese and English ultra-lightweight PP-OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) |
-| Chinese and English general PP-OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) |
-
-
-For more model downloads (including multiple languages), please refer to [PP-OCR series model downloads](./models_list_en.md).
-
-For a new language request, please refer to [Guideline for new language_requests](../../README.md#language_requests).
diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md
deleted file mode 100644
index dc2becd167..0000000000
--- a/doc/doc_en/quickstart_en.md
+++ /dev/null
@@ -1,311 +0,0 @@
-# PaddleOCR Quick Start
-
-**Note:** This tutorial mainly introduces the usage of PP-OCR series models, please refer to [PP-Structure Quick Start](../../ppstructure/docs/quickstart_en.md) for the quick use of document analysis related functions.
-
-- [1. Installation](#1-installation)
- - [1.1 Install PaddlePaddle](#11-install-paddlepaddle)
- - [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package)
-- [2. Easy-to-Use](#2-easy-to-use)
- - [2.1 Use by Command Line](#21-use-by-command-line)
- - [2.1.1 Chinese and English Model](#211-chinese-and-english-model)
- - [2.1.2 Multi-language Model](#212-multi-language-model)
- - [2.2 Use by Code](#22-use-by-code)
- - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese--english-model-and-multilingual-model)
-- [3. Summary](#3-summary)
-
-
-
-
-
-## 1. Installation
-
-
-
-### 1.1 Install PaddlePaddle
-
-> If you do not have a Python environment, please refer to [Environment Preparation](./environment_en.md).
-
-- If you have CUDA 11 installed on your machine, please run the following command to install
-
- ```bash
- pip install paddlepaddle-gpu
- ```
-
-- If you have no available GPU on your machine, please run the following command to install the CPU version
-
- ```bash
- python -m pip install paddlepaddle
- ```
-
-For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/en/install/quick) for operation.
-
-
-
-### 1.2 Install PaddleOCR Whl Package
-
-```bash
-pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
-```
-
-
-
-## 2. Easy-to-Use
-
-
-
-### 2.1 Use by Command Line
-
-PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal
-
-```bash
-cd /path/to/ppocr_img
-```
-
-If you do not use the provided test image, you can replace the following `--image_dir` parameter with the corresponding test image path
-
-
-
-#### 2.1.1 Chinese and English Model
-
-* Detection, direction classification and recognition: set the parameter`--use_gpu false` to disable the gpu device
-
- ```bash
- paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false
- ```
-
- Output will be a list, each item contains bounding box, text and recognition confidence
-
- ```bash
- [[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
- [[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
- [[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
- ......
- ```
-
- pdf file is also supported, you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
-
- ```bash
- paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
- ```
-
-* Only detection: set `--rec` to `false`
-
- ```bash
- paddleocr --image_dir ./imgs_en/img_12.jpg --rec false
- ```
-
- Output will be a list, each item only contains bounding box
-
- ```bash
- [[397.0, 802.0], [1092.0, 802.0], [1092.0, 841.0], [397.0, 841.0]]
- [[397.0, 750.0], [1211.0, 750.0], [1211.0, 789.0], [397.0, 789.0]]
- [[397.0, 702.0], [1209.0, 698.0], [1209.0, 734.0], [397.0, 738.0]]
- ......
- ```
-
-* Only recognition: set `--det` to `false`
-
- ```bash
- paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en
- ```
-
- Output will be a list, each item contains text and recognition confidence
-
- ```bash
- ['PAIN', 0.9934559464454651]
- ```
-
-**Version**
-paddleocr uses the PP-OCRv4 model by default(`--ocr_version PP-OCRv4`). If you want to use other versions, you can set the parameter `--ocr_version`, the specific version description is as follows:
-| version name | description |
-| --- | --- |
-| PP-OCRv4 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
-| PP-OCRv3 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
-| PP-OCRv2 | only supports Chinese and English detection and recognition, direction classifier, multilingual model is not updated |
-| PP-OCR | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
-
-If you want to add your own trained model, you can add model links and keys in [paddleocr](../../paddleocr.py) and recompile.
-
-More whl package usage can be found in [whl package](./whl_en.md)
-
-
-
-#### 2.1.2 Multi-language Model
-
-PaddleOCR currently supports 80 languages, which can be switched by modifying the `--lang` parameter.
-
-``` bash
-paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
-```
-
-
-
-
-
- 
- ', '', ...]}, # HTML string of the table
- 'cells': [
- {
- 'tokens': ['P', 'a', 'd', 'd', 'l', 'e', 'P', 'a', 'd', 'd', 'l', 'e'], # text in cell
- 'bbox': [x0, y0, x1, y1] # bbox of cell
- }
- ]
- }
-}
-```
-
-The default storage path for training data is `PaddleOCR/train_data`, if you already have a dataset on disk, just create a soft link to the dataset directory:
-
-```
-# linux and mac os
-ln -sf /train_data/dataset
-# windows
-mklink /d /train_data/dataset
-```
-
-## 1.2. Data Download
-
-Download the public dataset reference [table_datasets](dataset/table_datasets_en.md)。
-
-## 1.3. Dataset Generation
-
-Use [TableGeneration](https://github.com/WenmuZhou/TableGeneration) to generate scanned table images.
-
-TableGeneration is an open source table dataset generation tool, which renders html strings through browser rendering to obtain table images.
-
-Some samples are as follows:
-
-|Type|Sample|
-|---|---|
-|Simple Table||
-|Simple Color Table||
-
-## 1.4 Data annotation
-
-Data annotation can refer to[PPOCRLabel](https://github.com/PFCCLab/PPOCRLabel/blob/main/README.md)
-
-# 2. Training
-
-PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the [SLANet](../../configs/table/SLANet.yml) model will be used as an example:
-
-## 2.1. Start Training
-
-*If you are installing the cpu version, please modify the `use_gpu` field in the configuration file to false*
-
-```
-# GPU training Support single card and multi-card training
-# The training log will be automatically saved as train.log under "{save_model_dir}"
-
-# specify the single card training(Long training time, not recommended)
-python3 tools/train.py -c configs/table/SLANet.yml
-
-# specify the card number through --gpus
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/SLANet.yml
-```
-
-After starting training normally, you will see the following log output:
-
-```
-[2022/08/16 03:07:33] ppocr INFO: epoch: [1/400], global_step: 20, lr: 0.000100, acc: 0.000000, loss: 3.915012, structure_loss: 3.229450, loc_loss: 0.670590, avg_reader_cost: 2.63382 s, avg_batch_cost: 6.32390 s, avg_samples: 48.0, ips: 7.59025 samples/s, eta: 9 days, 2:29:27
-[2022/08/16 03:08:41] ppocr INFO: epoch: [1/400], global_step: 40, lr: 0.000100, acc: 0.000000, loss: 1.750859, structure_loss: 1.082116, loc_loss: 0.652822, avg_reader_cost: 0.02533 s, avg_batch_cost: 3.37251 s, avg_samples: 48.0, ips: 14.23271 samples/s, eta: 6 days, 23:28:43
-[2022/08/16 03:09:46] ppocr INFO: epoch: [1/400], global_step: 60, lr: 0.000100, acc: 0.000000, loss: 1.395154, structure_loss: 0.776803, loc_loss: 0.625030, avg_reader_cost: 0.02550 s, avg_batch_cost: 3.26261 s, avg_samples: 48.0, ips: 14.71214 samples/s, eta: 6 days, 5:11:48
-```
-
-The following information is automatically printed in the log:
-
-| Field | Meaning |
-| :----: | :------: |
-| epoch | current iteration round |
-| global_step | current iteration count |
-| lr | current learning rate |
-| acc | The accuracy of the current batch |
-| loss | current loss function |
-| structure_loss | Table Structure Loss Values |
-| loc_loss | Cell Coordinate Loss Value |
-| avg_reader_cost | Current batch data processing time |
-| avg_batch_cost | The total time spent in the current batch |
-| avg_samples | The number of samples in the current batch |
-| ips | Number of images processed per second |
-
-
-PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/table/SLANet.yml` to set the evaluation frequency. By default, it is evaluated once every 1000 iters. During the evaluation process, the best acc model is saved as `output/SLANet/best_accuracy` by default.
-
-If the validation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or perform evaluation after training.
-
-**Tips:** You can use the -c parameter to select various model configurations under the `configs/table/` path for training. For the table recognition algorithms supported by PaddleOCR, please refer to [Table Algorithms List](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_overview_en.md#3):
-
-**Note that the configuration file for prediction/evaluation must be the same as training. **
-
-## 2.2. Resume Training
-
-If the training program is interrupted, if you want to load the interrupted model to resume training, you can specify the path of the model to be loaded by specifying Global.checkpoints:
-
-```shell
-python3 tools/train.py -c configs/table/SLANet.yml -o Global.checkpoints=./your/trained/model
-```
-**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrained_model`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If `Global.checkpoints` The specified model path is incorrect, and the model specified by `Global.pretrained_model` will be loaded.
-
-## 2.3. Training with New Backbone
-
-The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
-necks->heads).
-
-```bash
-├── architectures # Code for building network
-├── transforms # Image Transformation Module
-├── backbones # Feature extraction module
-├── necks # Feature enhancement module
-└── heads # Output module
-```
-
-If the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the `Backbone` part of the configuration yml file.
-
-However, if you want to use a new Backbone, an example of replacing the backbones is as follows:
-
-1. Create a new file under the [ppocr/modeling/backbones](../../ppocr/modeling/backbones) folder, such as my_backbone.py.
-2. Add code in the my_backbone.py file, the sample code is as follows:
-
-```python
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-
-class MyBackbone(nn.Layer):
- def __init__(self, *args, **kwargs):
- super(MyBackbone, self).__init__()
- # your init code
- self.conv = nn.xxxx
-
- def forward(self, inputs):
- # your network forward
- y = self.conv(inputs)
- return y
-```
-
-3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py) file.
-
-After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:
-
-```yaml
- Backbone:
- name: MyBackbone
- args1: args1
-```
-
-**NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
-
-## 2.4. Mixed Precision Training
-
-If you want to speed up your training further, you can use [Auto Mixed Precision Training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html), taking a single machine and a single gpu as an example, the commands are as follows:
-
-```shell
-python3 tools/train.py -c configs/table/SLANet.yml \
- -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy \
- Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
- ```
-
-## 2.5. Distributed Training
-
-During multi-machine multi-gpu training, use the `--ips` parameter to set the used machine IP address, and the `--gpus` parameter to set the used GPU ID:
-
-```bash
-python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/table/SLANet.yml \
- -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy
-```
-
-
-**Note:** (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`. (3) For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](./distributed_training_en.md).
-
-## 2.6. Training on other platform(Windows/macOS/Linux DCU)
-
-- Windows GPU/CPU
-The Windows platform is slightly different from the Linux platform:
-Windows platform only supports `single gpu` training and inference, specify GPU for training `set CUDA_VISIBLE_DEVICES=0`
-On the Windows platform, DataLoader only supports single-process mode, so you need to set `num_workers` to 0;
-
-- macOS
-GPU mode is not supported, you need to set `use_gpu` to False in the configuration file, and the rest of the training evaluation prediction commands are exactly the same as Linux GPU.
-
-- Linux DCU
-Running on a DCU device requires setting the environment variable `export HIP_VISIBLE_DEVICES=0,1,2,3`, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.
-
-
-## 2.7. Fine-tuning
-
-
-### 2.7.1 Dataset
-
-Data number: It is recommended to prepare at least 2000 table recognition datasets for model fine-tuning.
-
-### 2.7.2 model selection
-
-It is recommended to choose the SLANet model (configuration file: [SLANet_ch.yml](../../configs/table/SLANet_ch.yml), pre-training model: [ch_ppstructure_mobile_v2.0_SLANet_train.tar](https://paddleocr.bj.bcebos .com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar)) for fine-tuning, its accuracy and generalization performance is the best Chinese table pre-training model currently available.
-
-For more table recognition models, please refer to [PP-Structure Series Model Library](../../ppstructure/docs/models_list.md).
-
-### 2.7.3 Training hyperparameter selection
-
-When fine-tuning the model, the most important hyperparameters are the pretrained model path `pretrained_model`, the learning rate `learning_rate`, and some configuration files are shown below.
-
-```yaml
-Global:
- pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # Pre-trained model path
-Optimizer:
- lr:
- name: Cosine
- learning_rate: 0.001 #
- warmup_epoch: 0
- regularizer:
- name: 'L2'
- factor: 0
-```
-
-In the above configuration file, you first need to specify the `pretrained_model` field as the `best_accuracy.pdparams` file path.
-
-The configuration file provided by PaddleOCR is for 4-card training (equivalent to a total batch size of `4*48=192`) and no pre-trained model is loaded. Therefore, in your scenario, the learning rate is the same as the total The batch size needs to be adjusted linearly, for example
-
-* If your scenario is single card training, single card batch_size=48, then the total batch_size=48, it is recommended to adjust the learning rate to about `0.00025`.
-* If your scenario is for single-card training, due to memory limitations, you can only set batch_size=32 for a single card, then the total batch_size=32, it is recommended to adjust the learning rate to about `0.00017`.
-
-# 3. Evaluation and Test
-
-## 3.1. Evaluation
-
-The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating metrics, you need to set `Global.checkpoints` to point to the saved parameter file. Evaluation datasets can be modified via the `label_file_list` setting in Eval via `configs/table/SLANet.yml`.
-
-```
-# GPU evaluation, Global.checkpoints is the weight to be tested
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/table/SLANet.yml -o Global.checkpoints={path/to/weights}/best_accuracy
-```
-
-After the operation is completed, the acc indicator of the model will be output. If you evaluate the English table recognition model, you will see the following output.
-
-```bash
-[2022/08/16 07:59:55] ppocr INFO: acc:0.7622245132160782
-[2022/08/16 07:59:55] ppocr INFO: fps:30.991640622573044
-```
-
-## 3.2. Test table structure recognition effect
-
-Using the model trained by PaddleOCR, you can quickly get prediction through the following script.
-
-The default prediction picture is stored in `infer_img`, and the trained weight is specified via `-o Global.checkpoints`:
-
-
-According to the `save_model_dir` and `save_epoch_step` fields set in the configuration file, the following parameters will be saved:
-
-
-```
-output/SLANet/
-├── best_accuracy.pdopt
-├── best_accuracy.pdparams
-├── best_accuracy.states
-├── config.yml
-├── latest.pdopt
-├── latest.pdparams
-├── latest.states
-└── train.log
-```
-Among them, best_accuracy.* is the best model on the evaluation set; latest.* is the model of the last epoch.
-
-```
-# Predict table image
-python3 tools/infer_table.py -c configs/table/SLANet.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=ppstructure/docs/table/table.jpg
-```
-
-Input image:
-
-
-
-Get the prediction result of the input image:
-
-```
-['', '', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' ', '', ''],[[320.0562438964844, 197.83375549316406, 350.0928955078125, 214.4309539794922], ... , [318.959228515625, 271.0166931152344, 353.7394104003906, 286.4538269042969]]
-```
-
-The cell coordinates are visualized as
-
-
-
-# 4. Model export and prediction
-
-## 4.1 Model export
-
-inference model (model saved by `paddle.jit.save`)
-Generally, it is model training, a solidified model that saves the model structure and model parameters in a file, and is mostly used to predict deployment scenarios.
-The model saved during the training process is the checkpoints model, and only the parameters of the model are saved, which are mostly used to resume training.
-Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting deployment and accelerating reasoning, and is flexible and convenient, and is suitable for actual system integration.
-
-The way to convert the form recognition model to the inference model is the same as the text detection and recognition, as follows:
-
-```
-# -c Set the training algorithm yml configuration file
-# -o Set optional parameters
-# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.save_inference_dir Set the address where the converted model will be saved.
-
-python3 tools/export_model.py -c configs/table/SLANet.yml -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy Global.save_inference_dir=./inference/SLANet/
-```
-
-After the conversion is successful, there are three files in the model save directory:
-
-
-```
-inference/SLANet/
- ├── inference.pdiparams # The parameter file of inference model
- ├── inference.pdiparams.info # The parameter information of inference model, which can be ignored
- └── inference.pdmodel # The program file of model
-```
-
-## 4.2 Prediction
-
-After the model is exported, use the following command to complete the prediction of the inference model
-
-```python
-python3.7 table/predict_structure.py \
- --table_model_dir={path/to/inference model} \
- --table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
- --image_dir=docs/table/table.jpg \
- --output=../output/table
-```
-
-Input image:
-
-
-
-Get the prediction result of the input image:
-
-```
-['', '', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', '', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' | ', ' | ', ' | ', ' | ', ' | ', ' ', '', ' ', '', ''],[[320.0562438964844, 197.83375549316406, 350.0928955078125, 214.4309539794922], ... , [318.959228515625, 271.0166931152344, 353.7394104003906, 286.4538269042969]]
-```
-
-The cell coordinates are visualized as
-
-
-
-
-
-# 5. FAQ
-
-Q1: After the training model is transferred to the inference model, the prediction effect is inconsistent?
-
-**A**: There are many such problems, and the problems are mostly caused by inconsistent preprocessing and postprocessing parameters when the trained model predicts and the preprocessing and postprocessing parameters when the inference model predicts. You can compare whether there are differences in preprocessing, postprocessing, and prediction in the configuration files used for training.
diff --git a/doc/doc_en/training_en.md b/doc/doc_en/training_en.md
deleted file mode 100644
index 86c4deb386..0000000000
--- a/doc/doc_en/training_en.md
+++ /dev/null
@@ -1,154 +0,0 @@
-# Model Training
-
-- [1.Yml Configuration ](#1-Yml-Configuration)
-- [2. Basic Concepts](#1-basic-concepts)
- * [2.1 Learning Rate](#11-learning-rate)
- * [2.2 Regularization](#12-regularization)
- * [2.3 Evaluation Indicators](#13-evaluation-indicators-)
-- [3. Data and Vertical Scenes](#2-data-and-vertical-scenes)
- * [3.1 Training Data](#21-training-data)
- * [3.2 Vertical Scene](#22-vertical-scene)
- * [3.3 Build Your Own Dataset](#23-build-your-own-data-set)
-* [4. FAQ](#3-faq)
-
-
-This article will introduce the basic concepts that is necessary for model training and tuning.
-
-At the same time, it will briefly introduce the structure of the training data and how to prepare the data to fine-tune model in vertical scenes.
-
-
-
-## 1. Yml Configuration
-
-The PaddleOCR uses configuration files to control network training and evaluation parameters. In the configuration file, you can set the model, optimizer, loss function, and pre- and post-processing parameters of the model. PaddleOCR reads these parameters from the configuration file, and then builds a complete training process to train the model. Fine-tuning can also be completed by modifying the parameters in the configuration file, which is simple and convenient.
-
-For the complete configuration file description, please refer to [Configuration File](./config_en.md)
-
-
-
-## 2. Basic Concepts
-
-During the model training process, some hyper-parameters can be manually specified to obtain the optimal result at the least cost. Different data volumes may require different hyper-parameters. When you want to fine-tune the model based on your own data, there are several parameter adjustment strategies for reference:
-
-
-### 2.1 Learning Rate
-
-The learning rate is one of the most important hyper-parameters for training neural networks. It represents the step length of the gradient moving towards the optimal solution of the loss function in each iteration.
-A variety of learning rate update strategies are provided by PaddleOCR, which can be specified in configuration files. For example,
-
-```
-Optimizer:
- ...
- lr:
- name: Piecewise
- decay_epochs : [700, 800]
- values : [0.001, 0.0001]
- warmup_epoch: 5
-```
-
-`Piecewise` stands for piece-wise constant attenuation. Different learning rates are specified in different learning stages, and the learning rate stay the same in each stage.
-
-`warmup_epoch` means that in the first 5 epochs, the learning rate will be increased gradually from 0 to base_lr. For all strategies, please refer to the code [learning_rate.py](../../ppocr/optimizer/learning_rate.py).
-
-
-### 2.2 Regularization
-
-Regularization can effectively avoid algorithm over-fitting. PaddleOCR provides L1 and L2 regularization methods.
-L1 and L2 regularization are the most widely used regularization methods.
-L1 regularization adds a regularization term to the objective function to reduce the sum of absolute values of the parameters;
-while in L2 regularization, the purpose of adding a regularization term is to reduce the sum of squared parameters.
-The configuration method is as follows:
-
-```
-Optimizer:
- ...
- regularizer:
- name: L2
- factor: 2.0e-05
-```
-
-### 2.3 Evaluation Indicators
-
-(1) Detection stage: First, evaluate according to the IOU of the detection frame and the labeled frame. If the IOU is greater than a certain threshold, it is judged that the detection is accurate. Here, the detection frame and the label frame are different from the general general target detection frame, and they are represented by polygons. Detection accuracy: the percentage of the correct detection frame number in all detection frames is mainly used to judge the detection index. Detection recall rate: the percentage of correct detection frames in all marked frames, which is mainly an indicator of missed detection.
-
-(2) Recognition stage: Character recognition accuracy, that is, the ratio of correctly recognized text lines to the number of marked text lines. Only the entire line of text recognition pairs can be regarded as correct recognition.
-
-(3) End-to-end statistics: End-to-end recall rate: accurately detect and correctly identify the proportion of text lines in all labeled text lines; End-to-end accuracy rate: accurately detect and correctly identify the number of text lines in the detected text lines The standard for accurate detection is that the IOU of the detection box and the labeled box is greater than a certain threshold, and the text in the correctly identified detection box is the same as the labeled text.
-
-
-
-## 3. Data and Vertical Scenes
-
-
-
-### 3.1 Training Data
-
-The current open source models, data sets and magnitudes are as follows:
-
-- Detection:
- - English data set, ICDAR2015
- - Chinese data set, LSVT street view data set training data 3w pictures
-
-- Identification:
- - English data set, MJSynth and SynthText synthetic data, the data volume is tens of millions.
- - Chinese data set, LSVT street view data set crops the image according to the truth value, and performs position calibration, a total of 30w images. In addition, based on the LSVT corpus, 500w of synthesized data.
- - Small language data set, using different corpora and fonts, respectively generated 100w synthetic data set, and using ICDAR-MLT as the verification set.
-
-Among them, the public data sets are all open source, users can search and download by themselves, or refer to [Chinese data set](dataset/datasets_en.md), synthetic data is not open source, users can use open source synthesis tools to synthesize by themselves. Synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) etc.
-
-
-
-### 3.2 Vertical Scene
-
-PaddleOCR mainly focuses on general OCR. If you have vertical requirements, you can use PaddleOCR + vertical data to train yourself;
-If there is a lack of labeled data, or if you do not want to invest in research and development costs, it is recommended to directly call the open API, which covers some of the more common vertical categories.
-
-
-
-### 3.3 Build Your Own Dataset
-
-There are several experiences for reference when constructing the data set:
-
-(1) The amount of data in the training set:
-
- a. The data required for detection is relatively small. For Fine-tune based on the PaddleOCR model, 500 sheets are generally required to achieve good results.
- b. Recognition is divided into English and Chinese. Generally, English scenarios require hundreds of thousands of data to achieve good results, while Chinese requires several million or more.
-
-
-(2) When the amount of training data is small, you can try the following three ways to get more data:
-
- a. Manually collect more training data, the most direct and effective way.
- b. Basic image processing or transformation based on PIL and opencv. For example, the three modules of ImageFont, Image, ImageDraw in PIL write text into the background, opencv's rotating affine transformation, Gaussian filtering and so on.
- c. Use data generation algorithms to synthesize data, such as algorithms such as pix2pix.
-
-
-
-## 4. FAQ
-
-**Q**: How to choose a suitable network input shape when training CRNN recognition?
-
- A: The general height is 32, the longest width is selected, there are two methods:
-
- (1) Calculate the aspect ratio distribution of training sample images. The selection of the maximum aspect ratio considers 80% of the training samples.
-
- (2) Count the number of texts in training samples. The selection of the longest number of characters considers the training sample that satisfies 80%. Then the aspect ratio of Chinese characters is approximately considered to be 1, and that of English is 3:1, and the longest width is estimated.
-
-**Q**: During the recognition training, the accuracy of the training set has reached 90, but the accuracy of the verification set has been kept at 70, what should I do?
-
- A: If the accuracy of the training set is 90 and the test set is more than 70, it should be over-fitting. There are two methods to try:
-
- (1) Add more augmentation methods or increase the [probability] of augmented prob (https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/rec_img_aug.py#L341), The default is 0.4.
-
- (2) Increase the [l2 dcay value] of the system (https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
-
-**Q**: When the recognition model is trained, loss can drop normally, but acc is always 0
-
- A: It is normal for the acc to be 0 at the beginning of the recognition model training, and the indicator will come up after a longer training period.
-
-***
-
-Click the following links for detailed training tutorial:
-
-- [text detection model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)
-- [text recognition model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)
-- [text direction classification model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)
diff --git a/doc/doc_en/tree_en.md b/doc/doc_en/tree_en.md
deleted file mode 100644
index cb188bff50..0000000000
--- a/doc/doc_en/tree_en.md
+++ /dev/null
@@ -1,219 +0,0 @@
-# Overall directory structure
-
-The overall directory structure of PaddleOCR is introduced as follows:
-
-
-```
-PaddleOCR
-├── configs // Configuration file, you can config the model structure and modify the hyperparameters through the yml file
-│ ├── cls // Angle classifier config files
-│ │ ├── cls_mv3.yml // Training config, including backbone network, head, loss, optimizer and data
-│ ├── det // Text detection config files
-│ │ ├── det_mv3_db.yml // Training config
-│ │ ...
-│ └── rec // Text recognition config files
-│ ├── rec_mv3_none_bilstm_ctc.yml // CRNN config
-│ ...
-├── deploy // Depoly
-│ ├── android_demo // Android demo
-│ │ ...
-│ ├── cpp_infer // C++ infer
-│ │ ├── CMakeLists.txt // Cmake file
-│ │ ├── docs // Docs
-│ │ │ └── windows_vs2019_build.md
-│ │ ├── include // Head Files
-│ │ │ ├── clipper.h // clipper
-│ │ │ ├── config.h // Inference config
-│ │ │ ├── ocr_cls.h // Angle class
-│ │ │ ├── ocr_det.h // Text detection
-│ │ │ ├── ocr_rec.h // Text recognition
-│ │ │ ├── postprocess_op.h // Post-processing
-│ │ │ ├── preprocess_op.h // Pre-processing
-│ │ │ └── utility.h // tools
-│ │ ├── readme.md // Documentation
-│ │ ├── ...
-│ │ ├── src // Source code files
-│ │ │ ├── clipper.cpp
-│ │ │ ├── config.cpp
-│ │ │ ├── main.cpp
-│ │ │ ├── ocr_cls.cpp
-│ │ │ ├── ocr_det.cpp
-│ │ │ ├── ocr_rec.cpp
-│ │ │ ├── postprocess_op.cpp
-│ │ │ ├── preprocess_op.cpp
-│ │ │ └── utility.cpp
-│ │ └── tools // Compile and execute script
-│ │ ├── build.sh // Compile script
-│ │ ├── config.txt // Config file
-│ │ └── run.sh // Execute script
-│ ├── docker
-│ │ └── hubserving
-│ │ ├── cpu
-│ │ │ └── Dockerfile
-│ │ ├── gpu
-│ │ │ └── Dockerfile
-│ │ ├── README_cn.md
-│ │ ├── README.md
-│ │ └── sample_request.txt
-│ ├── hubserving // hubserving
-│ │ ├── ocr_cls // Angle class
-│ │ │ ├── config.json // Serving config
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py // Model
-│ │ │ └── params.py // Parameters
-│ │ ├── ocr_det // Text detection
-│ │ │ ├── config.json // serving config
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py // Model
-│ │ │ └── params.py // Parameters
-│ │ ├── ocr_rec // Text recognition
-│ │ │ ├── config.json
-│ │ │ ├── __init__.py
-│ │ │ ├── module.py
-│ │ │ └── params.py
-│ │ └── ocr_system // Inference System
-│ │ ├── config.json
-│ │ ├── __init__.py
-│ │ ├── module.py
-│ │ └── params.py
-│ ├── imgs // Inference images
-│ │ ├── cpp_infer_pred_12.png
-│ │ └── demo.png
-│ ├── ios_demo // IOS demo
-│ │ ...
-│ ├── lite // Lite depoly
-│ │ ├── cls_process.cc // Pre-process for angle class
-│ │ ├── cls_process.h
-│ │ ├── config.txt // Config file
-│ │ ├── crnn_process.cc // Pre-process for CRNN
-│ │ ├── crnn_process.h
-│ │ ├── db_post_process.cc // Pre-process for DB
-│ │ ├── db_post_process.h
-│ │ ├── Makefile // Compile file
-│ │ ├── ocr_db_crnn.cc // Inference system
-│ │ ├── prepare.sh // Prepare bash script
-│ │ ├── readme.md // Documentation
-│ │ ...
-│ ├── pdserving // Pdserving depoly
-│ │ ├── det_local_server.py // Text detection fast version, easy to deploy and fast to predict
-│ │ ├── det_web_server.py // Text detection full version, high stability distributed deployment
-│ │ ├── ocr_local_server.py // Text detection + recognition fast version
-│ │ ├── ocr_web_client.py // client
-│ │ ├── ocr_web_server.py // Text detection + recognition full version
-│ │ ├── readme.md // Documentation
-│ │ ├── rec_local_server.py // Text recognition fast version
-│ │ └── rec_web_server.py // Text recognition full version
-│ └── slim
-│ └── quantization // Quantization
-│ ├── export_model.py // Export model
-│ ├── quant.py // Quantization script
-│ └── README.md // Documentation
-├── doc // Documentation and Tutorials
-│ ...
-├── ppocr // Core code
-│ ├── data // Data processing
-│ │ ├── imaug // Image and label processing code
-│ │ │ ├── text_image_aug // Tia data augment for text recognition
-│ │ │ │ ├── __init__.py
-│ │ │ │ ├── augment.py // Tia_distort,tia_stretch and tia_perspective
-│ │ │ │ ├── warp_mls.py
-│ │ │ ├── __init__.py
-│ │ │ ├── east_process.py // Data processing steps of EAST algorithm
-│ │ │ ├── iaa_augment.py // Data augmentation operations
-│ │ │ ├── label_ops.py // label encode operations
-│ │ │ ├── make_border_map.py // Generate boundary map
-│ │ │ ├── make_shrink_map.py // Generate shrink graph
-│ │ │ ├── operators.py // Basic image operations, such as reading and normalization
-│ │ │ ├── randaugment.py // Random data augmentation operation
-│ │ │ ├── random_crop_data.py // Random crop
-│ │ │ ├── rec_img_aug.py // Data augmentation for text recognition
-│ │ │ └── sast_process.py // Data processing steps of SAST algorithm
-│ │ ├── __init__.py // Construct dataloader code
-│ │ ├── lmdb_dataset.py // Read lmdb dataset
-│ │ ├── simple_dataset.py // Read the dataset stored in text format
-│ ├── losses // Loss function
-│ │ ├── __init__.py // Construct loss code
-│ │ ├── cls_loss.py // Angle class loss
-│ │ ├── det_basic_loss.py // Text detection basic loss
-│ │ ├── det_db_loss.py // DB loss
-│ │ ├── det_east_loss.py // EAST loss
-│ │ ├── det_sast_loss.py // SAST loss
-│ │ ├── rec_ctc_loss.py // CTC loss
-│ │ ├── rec_att_loss.py // Attention loss
-│ ├── metrics // Metrics
-│ │ ├── __init__.py // Construct metric code
-│ │ ├── cls_metric.py // Angle class metric
-│ │ ├── det_metric.py // Text detection metric
- │ ├── eval_det_iou.py // Text detection iou code
-│ │ ├── rec_metric.py // Text recognition metric
-│ ├── modeling // Network
-│ │ ├── architectures // Architecture
-│ │ │ ├── __init__.py // Construct model code
-│ │ │ ├── base_model.py // Base model
-│ │ ├── backbones // backbones
-│ │ │ ├── __init__.py // Construct backbone code
-│ │ │ ├── det_mobilenet_v3.py // Text detection mobilenet_v3
-│ │ │ ├── det_resnet_vd.py // Text detection resnet
-│ │ │ ├── det_resnet_vd_sast.py // Text detection resnet backbone of the SAST algorithm
-│ │ │ ├── rec_mobilenet_v3.py // Text recognition mobilenet_v3
-│ │ │ └── rec_resnet_vd.py // Text recognition resnet
-│ │ ├── necks // Necks
-│ │ │ ├── __init__.py // Construct neck code
-│ │ │ ├── db_fpn.py // Standard fpn
-│ │ │ ├── east_fpn.py // EAST algorithm fpn network
-│ │ │ ├── sast_fpn.py // SAST algorithm fpn network
-│ │ │ ├── rnn.py // Character recognition sequence encoding
-│ │ ├── heads // Heads
-│ │ │ ├── __init__.py // Construct head code
-│ │ │ ├── cls_head.py // Angle class head
-│ │ │ ├── det_db_head.py // DB head
-│ │ │ ├── det_east_head.py // EAST head
-│ │ │ ├── det_sast_head.py // SAST head
-│ │ │ ├── rec_ctc_head.py // CTC head
-│ │ │ ├── rec_att_head.py // Attention head
-│ │ ├── transforms // Transforms
-│ │ │ ├── __init__.py // Construct transform code
-│ │ │ └── tps.py // TPS transform
-│ ├── optimizer // Optimizer
-│ │ ├── __init__.py // Construct optimizer code
-│ │ └── learning_rate.py // Learning rate decay
-│ │ └── optimizer.py // Optimizer
-│ │ └── regularizer.py // Network regularization
-│ ├── postprocess // Post-processing
-│ │ ├── cls_postprocess.py // Angle class post-processing
-│ │ ├── db_postprocess.py // DB post-processing
-│ │ ├── east_postprocess.py // EAST post-processing
-│ │ ├── locality_aware_nms.py // NMS
-│ │ ├── rec_postprocess.py // Text recognition post-processing
-│ │ └── sast_postprocess.py // SAST post-processing
-│ └── utils // utils
-│ ├── dict // Minor language dictionary
-│ ....
-│ ├── ic15_dict.txt // English number dictionary, case sensitive
-│ ├── ppocr_keys_v1.txt // Chinese dictionary for training Chinese models
-│ ├── logging.py // logger
-│ ├── save_load.py // Model saving and loading functions
-│ ├── stats.py // Training status statistics
-│ └── utility.py // Utility function
-├── tools
-│ ├── eval.py // Evaluation function
-│ ├── export_model.py // Export inference model
-│ ├── infer // Inference based on Inference engine
-│ │ ├── predict_cls.py
-│ │ ├── predict_det.py
-│ │ ├── predict_rec.py
-│ │ ├── predict_system.py
-│ │ └── utility.py
-│ ├── infer_cls.py // Angle classification inference based on training engine
-│ ├── infer_det.py // Text detection inference based on training engine
-│ ├── infer_rec.py // Text recognition inference based on training engine
-│ ├── program.py // Inference system
-│ ├── test_hubserving.py
-│ └── train.py // Start training script
-├── paddleocr.py
-├── README_ch.md // Chinese documentation
-├── README_en.md // English documentation
-├── README.md // Home page documentation
-├── requirements.txt // Requirements
-├── setup.py // Whl package packaging script
-├── train.sh // Start training bash script
diff --git a/doc/doc_en/tricks_en.md b/doc/doc_en/tricks_en.md
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index 92edce2666..0000000000
--- a/doc/doc_en/tricks_en.md
+++ /dev/null
@@ -1,68 +0,0 @@
-## Tricks
-Here we have sorted out some Chinese OCR training and prediction tricks, which are being updated continuously. You are welcome to contribute more OCR tricks ~
-
-- [Replace Backbone Network](#ReplaceBackboneNetwork)
-- [Long Chinese Text Recognition](#LongChineseTextRecognition)
-- [Space Recognition](#SpaceRecognition)
-
-
-#### 1、Replace Backbone Network
-- **Problem Description**
-
- At present, ResNet_vd series and MobileNetV3 series are the backbone networks used in PaddleOCR, whether replacing the other backbone networks will help to improve the accuracy? What should be paid attention to when replacing?
-
-- **Tips**
- - Whether text detection or text recognition, the choice of backbone network is a trade-off between prediction effect and prediction efficiency. Generally, a larger backbone network is selected, e.g. ResNet101_vd, then the performance of the detection or recognition is more accurate, but the time cost will increase accordingly. And a smaller backbone network is selected, e.g. MobileNetV3_small_x0_35, the prediction speed is faster, but the accuracy of detection or recognition will be reduced. Fortunately, the detection or recognition effect of different backbone networks is positively correlated with the performance of ImageNet 1000 classification task. [**PaddleClas**](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/en/models/models_intro_en.md) have sorted out the 23 series of classification network structures, such as ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet. It provides the top1 accuracy of classification, the time cost of GPU(V100 and T4) and CPU(SD 855), and the 117 pretrained models [**download addresses**](https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html).
-
- - Similar as the 4 stages of ResNet, the replacement of text detection backbone network is to determine those four stages to facilitate the integration of FPN like the object detection heads. In addition, for the text detection problem, the pre trained model in ImageNet1000 can accelerate the convergence and improve the accuracy.
-
- - In order to replace the backbone network of text recognition, we need to pay attention to the descending position of network width and height stride. Since the ratio between width and height is large in chinese text recognition, the frequency of height decrease is less and the frequency of width decrease is more. You can refer the [modifies of MobileNetV3](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py) in PaddleOCR.
-
-
-#### 2、Long Chinese Text Recognition
-- **Problem Description**
- The maximum resolution of Chinese recognition model during training is [3,32,320], if the text image to be recognized is too long, as shown in the figure below, how to adapt?
-
-
- 
-
- 
-
-
-
-
-
-
-
-
-
-
-
-
-
- 
-
- 
+ 
diff --git a/doc/doc_i18n/README_हिन्द.md b/doc/doc_i18n/README_हिन्द.md
index 0598647c8b..f8ab49ec52 100644
--- a/doc/doc_i18n/README_हिन्द.md
+++ b/doc/doc_i18n/README_हिन्द.md
@@ -1,7 +1,7 @@
[English](../../README_en.md) | [简体中文](../../README.md) | हिन्दी | [日本語](./README_日本語.md) | [한국인](./README_한국어.md) | [Pу́сский язы́к](./README_Ру́сский_язы́к.md)
-
+
diff --git a/doc/doc_i18n/README_日本語.md b/doc/doc_i18n/README_日本語.md
index 7d3121f3e2..9a1f97736e 100644
--- a/doc/doc_i18n/README_日本語.md
+++ b/doc/doc_i18n/README_日本語.md
@@ -1,7 +1,7 @@
[English](../../README_en.md) | [简体中文](../../README.md) | [हिन्दी](./README_हिन्द.md) | 日本語 | [한국인](./README_한국어.md) | [Pу́сский язы́к](./README_Ру́сский_язы́к.md)
-
+
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diff --git a/docs/index.en.md b/docs/index.en.md
index b6de403e95..bf8b6c4a7a 100644
--- a/docs/index.en.md
+++ b/docs/index.en.md
@@ -7,7 +7,7 @@ hide:
---
- 
+
diff --git a/docs/index.md b/docs/index.md
index c472aabca5..0a5ab834fa 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -7,7 +7,7 @@ hide:
---
-
+
diff --git a/docs/ppocr/model_train/images/en_paper.jpg b/docs/ppocr/model_train/images/en_paper.jpg
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diff --git a/doc/tricks/long_text_examples.jpg b/docs/ppocr/model_train/images/long_text_examples.jpg
similarity index 100%
rename from doc/tricks/long_text_examples.jpg
rename to docs/ppocr/model_train/images/long_text_examples.jpg
diff --git a/docs/ppocr/model_train/training.md b/docs/ppocr/model_train/training.md
index 4ce0e2dffa..6ef42c0922 100644
--- a/docs/ppocr/model_train/training.md
+++ b/docs/ppocr/model_train/training.md
@@ -63,13 +63,13 @@ Optimizer:
目前开源的模型，数据集和量级如下：
- 检测：
- - 英文数据集，ICDAR2015
- - 中文数据集，LSVT街景数据集训练数据3w张图片
+ - 英文数据集，ICDAR2015
+ - 中文数据集，LSVT街景数据集训练数据3w张图片
- 识别：
- - 英文数据集，MJSynth和SynthText合成数据，数据量上千万。
- - 中文数据集，LSVT街景数据集根据真值将图crop出来，并进行位置校准，总共30w张图像。此外基于LSVT的语料，合成数据500w。
- - 小语种数据集，使用不同语料和字体，分别生成了100w合成数据集，并使用ICDAR-MLT作为验证集。
+ - 英文数据集，MJSynth和SynthText合成数据，数据量上千万。
+ - 中文数据集，LSVT街景数据集根据真值将图crop出来，并进行位置校准，总共30w张图像。此外基于LSVT的语料，合成数据500w。
+ - 小语种数据集，使用不同语料和字体，分别生成了100w合成数据集，并使用ICDAR-MLT作为验证集。
其中，公开数据集都是开源的，用户可自行搜索下载，也可参考[中文数据集](../../datasets/datasets.md)，合成数据暂不开源，用户可使用开源合成工具自行合成，可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer) 、[SynthText](https://github.com/ankush-me/SynthText) 、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) 等。
@@ -101,17 +101,17 @@ c. 利用数据生成算法合成数据，例如pix2pix或[StyleText](https://gi
**Q**：训练CRNN识别时，如何选择合适的网络输入shape？
A：一般高度采用32，最长宽度的选择，有两种方法：
-
+
（1）统计训练样本图像的宽高比分布。最大宽高比的选取考虑满足80%的训练样本。
-
+
（2）统计训练样本文字数目。最长字符数目的选取考虑满足80%的训练样本。然后中文字符长宽比近似认为是1，英文认为3：1，预估一个最长宽度。
**Q**：识别训练时，训练集精度已经到达90了，但验证集精度一直在70，涨不上去怎么办？
A：训练集精度90，测试集70多的话，应该是过拟合了，有两个可尝试的方法：
-
+
（1）加入更多的增广方式或者调大增广prob的[概率](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/rec_img_aug.py#L341)，默认为0.4。
-
+
（2）调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
**Q**: 识别模型训练时，loss能正常下降，但acc一直为0
diff --git a/docs/ppocr/model_train/tricks.en.md b/docs/ppocr/model_train/tricks.en.md
new file mode 100644
index 0000000000..57ca27d922
--- /dev/null
+++ b/docs/ppocr/model_train/tricks.en.md
@@ -0,0 +1,65 @@
+---
+comments: true
+---
+
+### Tricks
+
+Here we have sorted out some Chinese OCR training and prediction tricks, which are being updated continuously. You are welcome to contribute more OCR tricks ~
+
+#### 1、Replace Backbone Network
+
+- **Problem Description**
+
+ At present, ResNet_vd series and MobileNetV3 series are the backbone networks used in PaddleOCR, whether replacing the other backbone networks will help to improve the accuracy? What should be paid attention to when replacing?
+
+- **Tips**
+ - Whether text detection or text recognition, the choice of backbone network is a trade-off between prediction effect and prediction efficiency. Generally, a larger backbone network is selected, e.g. ResNet101_vd, then the performance of the detection or recognition is more accurate, but the time cost will increase accordingly. And a smaller backbone network is selected, e.g. MobileNetV3_small_x0_35, the prediction speed is faster, but the accuracy of detection or recognition will be reduced. Fortunately, the detection or recognition effect of different backbone networks is positively correlated with the performance of ImageNet 1000 classification task. [**PaddleClas**](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/en/models/models_intro_en.md) have sorted out the 23 series of classification network structures, such as ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet. It provides the top1 accuracy of classification, the time cost of GPU(V100 and T4) and CPU(SD 855), and the 117 pretrained models [**download addresses**](https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html).
+
+ - Similar as the 4 stages of ResNet, the replacement of text detection backbone network is to determine those four stages to facilitate the integration of FPN like the object detection heads. In addition, for the text detection problem, the pre trained model in ImageNet1000 can accelerate the convergence and improve the accuracy.
+
+ - In order to replace the backbone network of text recognition, we need to pay attention to the descending position of network width and height stride. Since the ratio between width and height is large in chinese text recognition, the frequency of height decrease is less and the frequency of width decrease is more. You can refer the [modifies of MobileNetV3](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py) in PaddleOCR.
+
+#### 2、Long Chinese Text Recognition
+
+- **Problem Description**
+ The maximum resolution of Chinese recognition model during training is [3,32,320], if the text image to be recognized is too long, as shown in the figure below, how to adapt?
+
+ 
+
+- **Tips**
+
+ During the training, the training samples are not directly resized to [3,32,320]. At first, the height of samples are resized to 32 and keep the ratio between the width and the height. When the width is less than 320, the excess parts are padding 0. Besides, when the ratio between the width and the height of the samples is larger than 10, these samples will be ignored. When the prediction for one image, do as above, but do not limit the max ratio between the width and the height. When the prediction for an images batch, do as training, but the resized target width is the longest width of the images in the batch. [Code as following](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/tools/infer/predict_rec.py)：
+
+ ```python linenums="1"
+ def resize_norm_img(self, img, max_wh_ratio):
+ imgC, imgH, imgW = self.rec_image_shape
+ assert imgC == img.shape[2]
+ if self.character_type == "ch":
+ imgW = int((32 * max_wh_ratio))
+ h, w = img.shape[:2]
+ ratio = w / float(h)
+ if math.ceil(imgH * ratio) > imgW:
+ resized_w = imgW
+ else:
+ resized_w = int(math.ceil(imgH * ratio))
+ resized_image = cv2.resize(img, (resized_w, imgH))
+ resized_image = resized_image.astype('float32')
+ resized_image = resized_image.transpose((2, 0, 1)) / 255
+ resized_image -= 0.5
+ resized_image /= 0.5
+ padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+ padding_im[:, :, 0:resized_w] = resized_image
+ return padding_im
+ ```
+
+#### 3、Space Recognition
+
+- **Problem Description**
+
+ As shown in the figure below, for Chinese and English mixed scenes, in order to facilitate reading and using the recognition results, it is often necessary to recognize the spaces between words. How can this situation be adapted?
+
+ 
+
+- **Tips**
+
+ There are two possible methods for space recognition. (1) Optimize the text detection. For spliting the text at the space in detection results, it needs to divide the text line with space into many segments when label the data for detection. (2) Optimize the text recognition. The space character is introduced into the recognition dictionary. Label the blank line in the training data for text recognition. In addition, we can also concat multiple word lines to synthesize the training data with spaces. PaddleOCR currently uses the second method.
diff --git a/docs/ppocr/model_train/tricks.md b/docs/ppocr/model_train/tricks.md
new file mode 100644
index 0000000000..71ec3fcce4
--- /dev/null
+++ b/docs/ppocr/model_train/tricks.md
@@ -0,0 +1,65 @@
+---
+comments: true
+---
+
+### 技巧
+
+这里我们整理了一些中文OCR训练和预测技巧，持续更新中，欢迎大家贡献更多OCR技巧~
+
+#### 1、更换骨干网络
+
+- **问题描述**
+
+ 目前PaddleOCR使用的主干网络为ResNet_vd系列和MobileNetV3系列，更换其他主干网络是否有助于提高准确率？更换时需要注意什么？
+
+- **技巧**
+ - 无论是文本检测还是文本识别，主干网络的选择都是预测效果和预测效率的权衡。一般选择较大的主干网络，如ResNet101_vd，则检测或识别的性能更准确，但时间成本也会相应增加。而选择较小的主干网络，如MobileNetV3_small_x0_35，预测速度更快，但检测或识别的准确率会降低。幸运的是，不同骨干网络的检测或识别效果与ImageNet 1000分类任务的性能呈正相关。[**PaddleClas**](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/en/models/models_intro_en.md)整理了ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet等23个系列的分类网络结构，提供了分类top1准确率、GPU(V100和T4)和CPU(SD 855)的时间成本，以及117个预训练模型[**下载地址**](https://paddleclas-en.readthedocs.io/en/latest/models/models_intro_en.html)。
+
+- 和ResNet的4个阶段类似，文本检测骨干网络的更换就是确定这4个阶段，以便于像物体检测heads一样集成FPN。另外，对于文本检测问题，ImageNet1000中的预训练模型可以加速收敛并提高准确率。
+
+- 更换文本识别骨干网络时，需要注意网络宽度和高度步长的下降位置。由于中文文本识别中宽度和高度的比值较大，因此高度下降的频率较少，宽度下降的频率较多。可以参考PaddleOCR中[MobileNetV3的修改](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py)。
+
+#### 2、长中文文本识别
+
+- **问题描述**
+ 中文识别模型在训练时的最大分辨率为[3,32,320]，如果待识别的文本图像过长，如下图所示，该如何适配？
+
+ 
+
+- **小技巧**
+
+在训练时，不要直接将训练样本resize到[3,32,320]，先将样本的高度resize为32，并保持宽高比，当宽度小于320时，超出部分用0填充。另外，当样本的宽高比大于10时，这些样本将被忽略。对一张图片进行预测时，同上，但不限制最大宽高比。对一批图像进行预测时，按照训练的方式进行，但调整后的目标宽度是该批图像的最长宽度。 [代码如下](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/tools/infer/predict_rec.py)：
+
+ ```python linenums="1"
+ def resize_norm_img(self, img, max_wh_ratio):
+ imgC, imgH, imgW = self.rec_image_shape
+ assert imgC == img.shape[2]
+ if self.character_type == "ch":
+ imgW = int((32 * max_wh_ratio))
+ h, w = img.shape[:2]
+ ratio = w / float(h)
+ if math.ceil(imgH * ratio) > imgW:
+ resized_w = imgW
+ else:
+ resized_w = int(math.ceil(imgH * ratio))
+ resized_image = cv2.resize(img, (resized_w, imgH))
+ resized_image = resized_image.astype('float32')
+ resized_image = resized_image.transpose((2, 0, 1)) / 255
+ resized_image -= 0.5
+ resized_image /= 0.5
+ padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+ padding_im[:, :, 0:resized_w] = resized_image
+ return padding_im
+ ```
+
+#### 3、空格识别
+
+- **问题描述**
+
+如下图所示，对于中英文混合场景，为了方便阅读和使用识别结果，经常需要识别单词之间的空格，这种情况该如何适配？
+
+
+
+- **小技巧**
+
+空格识别有两种可能的方法。（1）优化文本检测。为了将检测结果中的文本分割在空格处，在对数据进行标记时，需要将带有空格的文本行分成许多段
diff --git a/mkdocs.yml b/mkdocs.yml
index 87c43b1854..3ccbfe3880 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -119,6 +119,7 @@ plugins:
文本方向分类器: Text Angle Classification
关键信息提取: Key Information Extraction
模型微调: Fine-tune
+ 训练tips: Training Tricks
模型压缩: Model Compression
模型量化: Model Quantization
模型裁剪: Model Prune
@@ -281,6 +282,7 @@ nav:
- 文本方向分类器: ppocr/model_train/angle_class.md
- 关键信息提取: ppocr/model_train/kie.md
- 模型微调: ppocr/model_train/finetune.md
+ - 训练tricks: ppocr/model_train/tricks.md
- 模型压缩:
- 模型量化: ppocr/model_compress/quantization.md
- 模型裁剪: ppocr/model_compress/prune.md
diff --git a/tests/test_cls_postprocess.py b/tests/test_cls_postprocess.py
index 51fbd8a647..668a1e53fc 100644
--- a/tests/test_cls_postprocess.py
+++ b/tests/test_cls_postprocess.py
@@ -1,8 +1,9 @@
import os
import sys
-import pytest
-import paddle
+
import numpy as np
+import paddle
+import pytest
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(current_dir, "..")))
diff --git a/doc/imgs_en/254.jpg b/tests/test_files/254.jpg
similarity index 100%
rename from doc/imgs_en/254.jpg
rename to tests/test_files/254.jpg
diff --git a/doc/imgs_en/img_10.jpg b/tests/test_files/img_10.jpg
similarity index 100%
rename from doc/imgs_en/img_10.jpg
rename to tests/test_files/img_10.jpg
diff --git a/tests/test_files/ppstructure/1.png b/tests/test_files/ppstructure/1.png
new file mode 100644
index 0000000000..faff6e3178
Binary files /dev/null and b/tests/test_files/ppstructure/1.png differ
diff --git a/tests/test_files/ppstructure/layout.jpg b/tests/test_files/ppstructure/layout.jpg
new file mode 100644
index 0000000000..c5c39dac72
Binary files /dev/null and b/tests/test_files/ppstructure/layout.jpg differ
diff --git a/tests/test_paddleocr_api.py b/tests/test_paddleocr.py
similarity index 59%
rename from tests/test_paddleocr_api.py
rename to tests/test_paddleocr.py
index 0af794d2b8..968d74f977 100644
--- a/tests/test_paddleocr_api.py
+++ b/tests/test_paddleocr.py
@@ -1,15 +1,15 @@
+# -*- encoding: utf-8 -*-
+from pathlib import Path
from typing import Any
import pytest
-from paddleocr import PaddleOCR, PPStructure
+from paddleocr import PaddleOCR
-# Test image paths
-IMAGE_PATHS_OCR = ["./doc/imgs_en/254.jpg", "./doc/imgs_en/img_10.jpg"]
-IMAGE_PATHS_STRUCTURE = [
- "./ppstructure/docs/table/layout.jpg",
- "./ppstructure/docs/table/1.png",
-]
+cur_dir = Path(__file__).resolve().parent
+test_file_dir = cur_dir / "test_files"
+
+IMAGE_PATHS_OCR = [str(test_file_dir / "254.jpg"), str(test_file_dir / "img_10.jpg")]
@pytest.fixture(params=["en", "ch"])
@@ -76,41 +76,3 @@ def test_ocr_rec_only(ocr_engine: PaddleOCR, image_path: str) -> None:
result = ocr_engine.ocr(image_path, det=False, rec=True)
assert result is not None
assert isinstance(result, list)
-
-
-@pytest.fixture(params=["en", "ch"])
-def structure_engine(request: Any) -> PPStructure:
- """
- Initialize PPStructure engine with different languages.
-
- Args:
- request: pytest fixture request object.
-
- Returns:
- An instance of PPStructure.
- """
- return PPStructure(lang=request.param)
-
-
-def test_structure_initialization(structure_engine: PPStructure) -> None:
- """
- Test PPStructure initialization.
-
- Args:
- structure_engine: An instance of PPStructure.
- """
- assert structure_engine is not None
-
-
-@pytest.mark.parametrize("image_path", IMAGE_PATHS_STRUCTURE)
-def test_structure_function(structure_engine: PPStructure, image_path: str) -> None:
- """
- Test PPStructure structure analysis functionality with different images.
-
- Args:
- structure_engine: An instance of PPStructure.
- image_path: Path to the image to be processed.
- """
- result = structure_engine(image_path)
- assert result is not None
- assert isinstance(result, list)
diff --git a/tests/test_ppstructure.py b/tests/test_ppstructure.py
new file mode 100644
index 0000000000..eb56dd33fe
--- /dev/null
+++ b/tests/test_ppstructure.py
@@ -0,0 +1,53 @@
+# -*- encoding: utf-8 -*-
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from paddleocr import PPStructure
+
+cur_dir = Path(__file__).resolve().parent
+test_file_dir = cur_dir / "test_files"
+
+IMAGE_PATHS_STRUCTURE = [
+ str(test_file_dir / "ppstructure" / "layout.jpg"),
+ str(test_file_dir / "ppstructure" / "1.png"),
+]
+
+
+@pytest.fixture(params=["en", "ch"])
+def structure_engine(request: Any) -> PPStructure:
+ """
+ Initialize PPStructure engine with different languages.
+
+ Args:
+ request: pytest fixture request object.
+
+ Returns:
+ An instance of PPStructure.
+ """
+ return PPStructure(lang=request.param)
+
+
+def test_structure_initialization(structure_engine: PPStructure) -> None:
+ """
+ Test PPStructure initialization.
+
+ Args:
+ structure_engine: An instance of PPStructure.
+ """
+ assert structure_engine is not None
+
+
+@pytest.mark.parametrize("image_path", IMAGE_PATHS_STRUCTURE)
+def test_structure_function(structure_engine: PPStructure, image_path: str) -> None:
+ """
+ Test PPStructure structure analysis functionality with different images.
+
+ Args:
+ structure_engine: An instance of PPStructure.
+ image_path: Path to the image to be processed.
+ """
+ result = structure_engine(image_path)
+ assert result is not None
+ assert isinstance(result, list)
| |
-
-
-
-
-
-
-
-
-
- 
- 
- 
-
-
-
-
-结果是一个list，每个item包含了文本框，文字和识别置信度
-```text
-[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
-[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
-[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
-[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
-[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
-[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
-[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
-......
-```
-
-* 识别预测
-
-```bash
-paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
-```
-
-结果是一个tuple，返回识别结果和识别置信度
-
-```text
-(0.99879867, 'LITTLE')
-```
-
-* 检测预测
-
-```
-paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
-```
-
-结果是一个list，每个item只包含文本框
-
-```
-[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
-[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
-[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
-......
-```
-
-
-### 2.2 python 脚本运行
-
-ppocr 也支持在python脚本中运行，便于嵌入到您自己的代码中 ：
-
-* 整图预测（检测+识别）
-
-```
-from paddleocr import PaddleOCR, draw_ocr
-
-# 同样也是通过修改 lang 参数切换语种
-ocr = PaddleOCR(lang="korean") # 首次执行会自动下载模型文件
-img_path = 'doc/imgs/korean_1.jpg '
-result = ocr.ocr(img_path)
-# 可通过参数控制单独执行识别、检测
-# result = ocr.ocr(img_path, det=False) 只执行识别
-# result = ocr.ocr(img_path, rec=False) 只执行检测
-# 打印检测框和识别结果
-for line in result:
- print(line)
-
-# 可视化
-from PIL import Image
-image = Image.open(img_path).convert('RGB')
-boxes = [line[0] for line in result]
-txts = [line[1][0] for line in result]
-scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
-im_show = Image.fromarray(im_show)
-im_show.save('result.jpg')
-```
-
-结果可视化:
-
-
- 
- 
-
-
-
-
-
-
-
-
-
-## 超轻量PP-OCRv2效果展示
- 
- 
- 
-
-
-
-
-
-## 多语言识别模型效果展示
-