diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 858dc02b9d..ecb0e9dfef 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -24,7 +24,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
### 1.1 文本检测算法
已支持的文本检测算法列表（戳链接获取使用教程）：
-- [x] [DB](./algorithm_det_db.md)
+- [x] [DB与DB++](./algorithm_det_db.md)
- [x] [EAST](./algorithm_det_east.md)
- [x] [SAST](./algorithm_det_sast.md)
- [x] [PSENet](./algorithm_det_psenet.md)
@@ -41,6 +41,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
|PSE|MobileNetV3|82.20%|70.48%|75.89%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|DB++|ResNet50|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
在Total-text文本检测公开数据集上，算法效果如下：
@@ -129,10 +130,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
已支持的关键信息抽取算法列表（戳链接获取使用教程）：
-- [x] [VI-LayoutXLM](./algorithm_kie_vi_laoutxlm.md)
-- [x] [LayoutLM](./algorithm_kie_laoutxlm.md)
-- [x] [LayoutLMv2](./algorithm_kie_laoutxlm.md)
-- [x] [LayoutXLM](./algorithm_kie_laoutxlm.md)
+- [x] [VI-LayoutXLM](./algorithm_kie_vi_layoutxlm.md)
+- [x] [LayoutLM](./algorithm_kie_layoutxlm.md)
+- [x] [LayoutLMv2](./algorithm_kie_layoutxlm.md)
+- [x] [LayoutXLM](./algorithm_kie_layoutxlm.md)
- [x] [SDMGR](././algorithm_kie_sdmgr.md)
在wildreceipt发票公开数据集上，算法复现效果如下：
diff --git a/doc/doc_en/algorithm_det_db_en.md b/doc/doc_en/algorithm_det_db_en.md
index f5f333a039..fde344c357 100644
--- a/doc/doc_en/algorithm_det_db_en.md
+++ b/doc/doc_en/algorithm_det_db_en.md
@@ -1,4 +1,4 @@
-# DB
+# DB && DB++
- [1. Introduction](#1)
- [2. Environment](#2)
@@ -21,13 +21,23 @@ Paper:
> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
> AAAI, 2020
+> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)
+> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang
+> TPAMI, 2022
+
On the ICDAR2015 dataset, the text detection result is as follows:
|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
| --- | --- | --- | --- | --- | --- | --- |
|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|DB++|ResNet50|[configs/det/det_r50_db++_ic15.yml](../../configs/det/det_r50_db++_ic15.yml)|90.89%|82.66%|86.58%|[pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
+On the TD_TR dataset, the text detection result is as follows:
+
+|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)|
## 2. Environment
@@ -96,4 +106,12 @@ More deployment schemes supported for DB:
pages={11474--11481},
year={2020}
}
-```
\ No newline at end of file
+
+@article{liao2022real,
+ title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion},
+ author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang},
+ journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+ year={2022},
+ publisher={IEEE}
+}
+```
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 5bf569e3e1..bca22f7848 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -22,7 +22,7 @@ Developers are welcome to contribute more algorithms! Please refer to [add new a
### 1.1 Text Detection Algorithms
Supported text detection algorithms (Click the link to get the tutorial):
-- [x] [DB](./algorithm_det_db_en.md)
+- [x] [DB && DB++](./algorithm_det_db_en.md)
- [x] [EAST](./algorithm_det_east_en.md)
- [x] [SAST](./algorithm_det_sast_en.md)
- [x] [PSENet](./algorithm_det_psenet_en.md)
@@ -39,6 +39,7 @@ On the ICDAR2015 dataset, the text detection result is as follows:
|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
|PSE|MobileNetV3|82.20%|70.48%|75.89%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|DB++|ResNet50|90.89%|82.66%|86.58%|[pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
On Total-Text dataset, the text detection result is as follows:
@@ -127,10 +128,10 @@ On the PubTabNet dataset, the algorithm result is as follows:
Supported KIE algorithms (Click the link to get the tutorial):
-- [x] [VI-LayoutXLM](./algorithm_kie_vi_laoutxlm_en.md)
-- [x] [LayoutLM](./algorithm_kie_laoutxlm_en.md)
-- [x] [LayoutLMv2](./algorithm_kie_laoutxlm_en.md)
-- [x] [LayoutXLM](./algorithm_kie_laoutxlm_en.md)
+- [x] [VI-LayoutXLM](./algorithm_kie_vi_layoutxlm_en.md)
+- [x] [LayoutLM](./algorithm_kie_layoutxlm_en.md)
+- [x] [LayoutLMv2](./algorithm_kie_layoutxlm_en.md)
+- [x] [LayoutXLM](./algorithm_kie_layoutxlm_en.md)
- [x] [SDMGR](./algorithm_kie_sdmgr_en.md)
On wildreceipt dataset, the algorithm result is as follows:
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index f776317006..749060a053 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -24,7 +24,7 @@ class BaseRecLabelDecode(object):
def __init__(self, character_dict_path=None, use_space_char=False):
self.beg_str = "sos"
self.end_str = "eos"
-
+ self.reverse = False
self.character_str = []
if character_dict_path is None:
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
@@ -38,6 +38,8 @@ class BaseRecLabelDecode(object):
if use_space_char:
self.character_str.append(" ")
dict_character = list(self.character_str)
+ if 'arabic' in character_dict_path:
+ self.reverse = True
dict_character = self.add_special_char(dict_character)
self.dict = {}
@@ -45,11 +47,6 @@ class BaseRecLabelDecode(object):
self.dict[char] = i
self.character = dict_character
- if 'arabic' in character_dict_path:
- self.reverse = True
- else:
- self.reverse = False
-
def pred_reverse(self, pred):
pred_re = []
c_current = ''
diff --git a/ppstructure/kie/README.md b/ppstructure/kie/README.md
index adb19a3ca7..d9471fb18d 100644
--- a/ppstructure/kie/README.md
+++ b/ppstructure/kie/README.md
@@ -242,9 +242,7 @@ For training, evaluation and inference tutorial for KIE models, please refer to
For training, evaluation and inference tutorial for text detection models, please refer to [text detection doc](../../doc/doc_en/detection_en.md).
-For training, evaluation and inference tutorial for text recognition models, please refer to [text recognition doc](../../doc/doc_en/recognition.md).
-
-If you want to finish the KIE tasks in your scene, and don't know what to prepare, please refer to [End cdoc](../../doc/doc_en/recognition.md).
+For training, evaluation and inference tutorial for text recognition models, please refer to [text recognition doc](../../doc/doc_en/recognition_en.md).
To complete the key information extraction task in your own scenario from data preparation to model selection, please refer to: [Guide to End-to-end KIE](./how_to_do_kie_en.md)。
diff --git a/ppstructure/pdf2word/pdf2word.md b/ppstructure/pdf2word/pdf2word.md
index 8d69d60748..564df4063e 100644
--- a/ppstructure/pdf2word/pdf2word.md
+++ b/ppstructure/pdf2word/pdf2word.md
@@ -1,11 +1,6 @@
# PDF2WORD
-PDF2WORD是PaddleOCR社区开发者@whj 基于PP-Structure智能文档分析模型实现的PDF转换Word应用程序，提供可直接安装的exe，方便windows用户运行
-
-
-
-