From 3f2d384faa95a1ad90b0475494f0624f2b900670 Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Mon, 11 May 2020 15:27:52 +0800
Subject: [PATCH 1/9] =?UTF-8?q?add=20doc=E3=80=81infer=5Fdet.py=E3=80=81re?=
 =?UTF-8?q?quirments.txt?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 configs/det/det_db_mv3.yml    |   1 +
 doc/detection.md              |  69 ++++++++++++++++
 doc/installation.md           |  25 ++++++
 requirments.txt               |   3 +
 tools/infer/predict_system.py |  26 +++++-
 tools/infer/utility.py        |  16 ++--
 tools/infer_det.py            | 148 ++++++++++++++++++++++++++++++++++
 7 files changed, 281 insertions(+), 7 deletions(-)
 create mode 100644 doc/detection.md
 create mode 100644 doc/installation.md
 create mode 100644 requirments.txt
 create mode 100755 tools/infer_det.py

diff --git a/configs/det/det_db_mv3.yml b/configs/det/det_db_mv3.yml
index a41c901e75..197b12044b 100755
--- a/configs/det/det_db_mv3.yml
+++ b/configs/det/det_db_mv3.yml
@@ -12,6 +12,7 @@ Global:
   image_shape: [3, 640, 640]
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
+  checkpoints:
   save_res_path: ./output/predicts_db.txt
   
 Architecture:
diff --git a/doc/detection.md b/doc/detection.md
new file mode 100644
index 0000000000..b8b8a9cc38
--- /dev/null
+++ b/doc/detection.md
@@ -0,0 +1,69 @@
+# 文字检测
+
+本节以icdar15数据集为例，介绍PaddleOCR中检测模型的使用方式。
+
+## 3.1 数据准备
+icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
+
+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。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
+，您可以通过wget的方式进行下载。
+```
+wget -P /PaddleOCR/train_data/  训练标注文件链接
+wget -P /PaddleOCR/train_data/  测试标注文件链接
+```
+
+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，分别是：
+```
+/PaddleOCR/train_data/  
+  └─ icdar_c4_train_imgs/         icdar数据集的训练数据
+  └─ ch4_test_images/             icdar数据集的测试数据
+  └─ train_icdar2015_label.txt    icdar数据集的训练标注
+  └─ test_icdar2015_label.txt     icdar数据集的测试标注
+```
+
+提供的标注文件格式为：
+```
+" 图像文件名                    json.dumps编码的图像标注信息"
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
+```
+json.dumps编码前的图像标注信息是包含多个字典的list，字典中的points表示文本框的位置，如果您想在其他数据集上训练PaddleOCR,
+可以按照上述形式构建标注文件。
+
+
+## 3.2 快速启动训练
+
+首先下载pretrain model，目前支持两种backbone，分别是MobileNetV3、ResNet50，您可以根据需求使用PaddleClas中的模型更换
+backbone。
+```
+# 下载MobileNetV3的预训练模型
+wget -P /PaddleOCR/pretrained_model/ 模型链接
+# 下载ResNet50的预训练模型
+wget -P /PaddleOCR/pretrained_model/ 模型链接
+```
+
+**启动训练**
+```
+cd PaddleOCR/
+python3 tools/train.py -c configs/det/det_db_mv3.yml
+```
+
+上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
+有关配置文件的详细解释，请参考[链接]()。
+
+您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
+```
+python3 tools/train.py -c configs/det/det_db_mv3.yml -o Optimizer.base_lr=0.0001
+```
+
+
+## 3.3 指标评估
+
+PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
+
+运行如下代码，根据配置文件det_db_mv3.yml中save_res_path指定的测试集检测结果文件，计算评估指标。
+
+```
+python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints ./output/best_accuracy
+```
+
+## 3.4 测试检测效果
diff --git a/doc/installation.md b/doc/installation.md
new file mode 100644
index 0000000000..5eca1dfe6a
--- /dev/null
+++ b/doc/installation.md
@@ -0,0 +1,25 @@
+### 2.1 快速安装
+
+我们提供了PaddleOCR开发环境的docker，您可以pull我们提供的docker运行PaddleOCR的环境。
+
+1. 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
+# 创建一个名字为pdocr的docker容器，并将当前目录映射到容器的/data目录下
+sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it paddlepaddle/paddle:1.7.2-gpu-cuda10.0-cudnn7  /bin/bash
+```
+
+2. 克隆PaddleOCR repo代码
+```
+apt-get update
+apt-get install git
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+3. 安装第三方库
+```
+cd PaddleOCR
+pip3 install --upgrade pip
+pip3 install -r requirements.txt
+```
diff --git a/requirments.txt b/requirments.txt
new file mode 100644
index 0000000000..e9bf32a993
--- /dev/null
+++ b/requirments.txt
@@ -0,0 +1,3 @@
+shapely
+imgaug
+pyclipper
\ No newline at end of file
diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py
index defa061595..4907a7ccf0 100755
--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -64,15 +64,39 @@ class TextSystem(object):
         if dt_boxes is None:
             return None, None
         img_crop_list = []
+
+        dt_boxes = sorted_boxes(dt_boxes)
+
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        #         self.print_draw_crop_rec_res(img_crop_list, rec_res)
+        # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         return dt_boxes, rec_res
 
 
+def sorted_boxes(dt_boxes):
+    """
+    Sort text boxes in order from top to bottom, left to right
+    args:
+        dt_boxes(array)：detected text boxes with shape [4, 2]
+    return:
+        sorted boxes(array) with shape [4, 2]
+    """
+    num_boxes = dt_boxes.shape[0]
+    sorted_boxes = sorted(dt_boxes, key=lambda x: x[0][1])
+    _boxes = list(sorted_boxes)
+
+    for i in range(num_boxes - 1):
+        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
+            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+            tmp = _boxes[i]
+            _boxes[i] = _boxes[i + 1]
+            _boxes[i + 1] = tmp
+    return _boxes
+
+
 if __name__ == "__main__":
     args = utility.parse_args()
     image_file_list = utility.get_image_file_list(args.image_dir)
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index a4f9f03daa..ff9586ab76 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -106,7 +106,7 @@ def create_predictor(args, mode):
     #             if args.use_fp16 else AnalysisConfig.Precision.Float32,
     #             max_batch_size=args.batch_size)
 
-    config.enable_memory_optim()
+    # config.enable_memory_optim()
     # use zero copy
     config.switch_use_feed_fetch_ops(False)
     predictor = create_paddle_predictor(config)
@@ -136,12 +136,16 @@ if __name__ == '__main__':
     args.det_model_dir = root_path + "test_models/public_v1/ch_det_mv3_db"
 
     predictor, input_tensor, output_tensors = create_predictor(args, mode='det')
-    print(predictor.get_input_names())
-    print(predictor.get_output_names())
-    print(predictor.program(), file=open("det_program.txt", 'w'))
+    print("det input", predictor.get_input_names())
+    print("det output", predictor.get_output_names())
+    # print(predictor.program(), file=open("det_program.txt", 'w'))
+    outputs = []
+    for output_tensor in output_tensors:
+        output = output_tensor.copy_to_cpu()
+        outputs.append(output)
 
     args.rec_model_dir = root_path + "test_models/public_v1/ch_rec_mv3_crnn/"
     rec_predictor, input_tensor, output_tensors = create_predictor(
         args, mode='rec')
-    print(rec_predictor.get_input_names())
-    print(rec_predictor.get_output_names())
+    print("rec input", rec_predictor.get_input_names())
+    print("rec output", rec_predictor.get_output_names())
diff --git a/tools/infer_det.py b/tools/infer_det.py
new file mode 100755
index 0000000000..d616323d9d
--- /dev/null
+++ b/tools/infer_det.py
@@ -0,0 +1,148 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import time
+import numpy as np
+from copy import deepcopy
+import json
+
+# from paddle.fluid.contrib.model_stat import summary
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect. 
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+from paddle import fluid
+from ppocr.utils.utility import create_module
+import program
+from ppocr.utils.save_load import init_model
+from ppocr.data.reader_main import reader_main
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+
+
+def draw_det_res(dt_boxes, config, img_name, ino):
+    if len(dt_boxes) > 0:
+        img_set_path = config['TestReader']['img_set_dir']
+        img_path = img_set_path + img_name
+        import cv2
+        src_im = cv2.imread(img_path)
+        for box in dt_boxes:
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+        save_det_path = os.path.basename(config['Global'][
+            'save_res_path']) + "/det_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, "det_{}.jpg".format(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The detected Image saved in {}".format(save_path))
+
+
+def main():
+    config = program.load_config(FLAGS.config)
+    program.merge_config(FLAGS.opt)
+    print(config)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    program.check_gpu(use_gpu)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    det_model = create_module(config['Architecture']['function'])(params=config)
+
+    startup_prog = fluid.Program()
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            _, eval_outputs = det_model(mode="test")
+            fetch_name_list = list(eval_outputs.keys())
+            eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
+
+    eval_prog = eval_prog.clone(for_test=True)
+    exe.run(startup_prog)
+
+    # load checkpoints
+    checkpoints = config['Global'].get('checkpoints')
+    if checkpoints:
+        path = checkpoints
+        fluid.load(eval_prog, path, exe)
+        logger.info("Finish initing model from {}".format(path))
+    else:
+        raise Exception("{} not exists!".format(checkpoints))
+
+    save_res_path = config['Global']['save_res_path']
+    with open(save_res_path, "wb") as fout:
+        test_reader = reader_main(config=config, mode='test')
+        tackling_num = 0
+        for data in test_reader():
+            img_num = len(data)
+            tackling_num = tackling_num + img_num
+            logger.info("tackling_num:%d", tackling_num)
+            img_list = []
+            ratio_list = []
+            img_name_list = []
+            for ino in range(img_num):
+                img_list.append(data[ino][0])
+                ratio_list.append(data[ino][1])
+                img_name_list.append(data[ino][2])
+            img_list = np.concatenate(img_list, axis=0)
+            outs = exe.run(eval_prog,\
+                feed={'image': img_list},\
+                fetch_list=eval_fetch_list)
+
+            global_params = config['Global']
+            postprocess_params = deepcopy(config["PostProcess"])
+            postprocess_params.update(global_params)
+            postprocess = create_module(postprocess_params['function'])\
+                (params=postprocess_params)
+            dt_boxes_list = postprocess(outs, ratio_list)
+            for ino in range(img_num):
+                dt_boxes = dt_boxes_list[ino]
+                img_name = img_name_list[ino]
+                dt_boxes_json = []
+                for box in dt_boxes:
+                    tmp_json = {"transcription": ""}
+                    tmp_json['points'] = box.tolist()
+                    dt_boxes_json.append(tmp_json)
+                otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
+                fout.write(otstr.encode())
+                draw_det_res(dt_boxes, config, img_name, ino)
+
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    parser = program.ArgsParser()
+    FLAGS = parser.parse_args()
+    main()

From af39902217bf6d02d489d258302cbe89fb53a427 Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Mon, 11 May 2020 15:33:54 +0800
Subject: [PATCH 2/9] fix tools/infer/utility.py

---
 tools/infer/utility.py | 25 ++-----------------------
 1 file changed, 2 insertions(+), 23 deletions(-)

diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index ff9586ab76..e8c76de9ff 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -99,7 +99,8 @@ def create_predictor(args, mode):
         config.disable_gpu()
 
     config.disable_glog_info()
-    config.switch_ir_optim(args.ir_optim)
+    # config.switch_ir_optim(args.ir_optim)
+
     #     if args.use_tensorrt:
     #         config.enable_tensorrt_engine(
     #             precision_mode=AnalysisConfig.Precision.Half
@@ -127,25 +128,3 @@ def draw_text_det_res(dt_boxes, img_path):
         cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
     img_name_pure = img_path.split("/")[-1]
     cv2.imwrite("./output/%s" % img_name_pure, src_im)
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    args.use_gpu = False
-    root_path = "/Users/liuweiwei06/Desktop/TEST_CODES/icode/baidu/personal-code/PaddleOCR/"
-    args.det_model_dir = root_path + "test_models/public_v1/ch_det_mv3_db"
-
-    predictor, input_tensor, output_tensors = create_predictor(args, mode='det')
-    print("det input", predictor.get_input_names())
-    print("det output", predictor.get_output_names())
-    # print(predictor.program(), file=open("det_program.txt", 'w'))
-    outputs = []
-    for output_tensor in output_tensors:
-        output = output_tensor.copy_to_cpu()
-        outputs.append(output)
-
-    args.rec_model_dir = root_path + "test_models/public_v1/ch_rec_mv3_crnn/"
-    rec_predictor, input_tensor, output_tensors = create_predictor(
-        args, mode='rec')
-    print("rec input", rec_predictor.get_input_names())
-    print("rec output", rec_predictor.get_output_names())

From 268ed03b3ec01af8ff2de9f9329659022c13bc5e Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Mon, 11 May 2020 19:59:07 +0800
Subject: [PATCH 3/9] update detection doc and infer code

---
 doc/detection.md                   | 16 +++++++--
 ppocr/data/det/db_process.py       |  3 --
 ppocr/data/reader_main.py          |  6 ----
 tools/eval_utils/eval_det_iou.py   |  4 +++
 tools/eval_utils/eval_det_utils.py |  8 +++++
 tools/infer/utility.py             |  8 -----
 tools/infer_det.py                 | 54 ++++++++++++++++++++++++++++--
 tools/program.py                   | 16 ---------
 8 files changed, 76 insertions(+), 39 deletions(-)

diff --git a/doc/detection.md b/doc/detection.md
index b8b8a9cc38..6124b74f16 100644
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -32,8 +32,8 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 
 ## 3.2 快速启动训练
 
-首先下载pretrain model，目前支持两种backbone，分别是MobileNetV3、ResNet50，您可以根据需求使用PaddleClas中的模型更换
-backbone。
+首先下载pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
+您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
 ```
 # 下载MobileNetV3的预训练模型
 wget -P /PaddleOCR/pretrained_model/ 模型链接
@@ -63,7 +63,17 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall
 运行如下代码，根据配置文件det_db_mv3.yml中save_res_path指定的测试集检测结果文件，计算评估指标。
 
 ```
-python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints ./output/best_accuracy
+python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints="./output/best_accuracy"
 ```
 
 ## 3.4 测试检测效果
+
+测试单张图像的检测效果
+```
+python3 tools/infer_det.py -c config/det/det_db_mv3.yml -o TestReader.single_img_path="./demo.jpg"
+```
+
+测试文件夹下所有图像的检测效果
+```
+python3 tools/infer_det.py -c config/det/det_db_mv3.yml -o TestReader.single_img_path="./demo_img/"
+```
diff --git a/ppocr/data/det/db_process.py b/ppocr/data/det/db_process.py
index 2a6393a166..faca9ac209 100644
--- a/ppocr/data/det/db_process.py
+++ b/ppocr/data/det/db_process.py
@@ -124,9 +124,6 @@ class DBProcessTest(object):
     def resize_image_type0(self, im):
         """
         resize image to a size multiple of 32 which is required by the network
-        :param im: the resized image
-        :param max_side_len: limit of max image size to avoid out of memory in gpu
-        :return: the resized image and the resize ratio
         """
         max_side_len = self.max_side_len
         h, w, _ = im.shape
diff --git a/ppocr/data/reader_main.py b/ppocr/data/reader_main.py
index 323620bc59..55bd1e0842 100755
--- a/ppocr/data/reader_main.py
+++ b/ppocr/data/reader_main.py
@@ -73,9 +73,3 @@ def reader_main(config=None, mode=None):
         return paddle.reader.multiprocess_reader(readers, False)
     else:
         return function(mode)
-
-
-def test_reader(image_shape, img_path):
-    img = cv2.imread(img_path)
-    norm_img = process_image(img, image_shape)
-    return norm_img
diff --git a/tools/eval_utils/eval_det_iou.py b/tools/eval_utils/eval_det_iou.py
index c6dacb3e1b..2f5ff2f10f 100644
--- a/tools/eval_utils/eval_det_iou.py
+++ b/tools/eval_utils/eval_det_iou.py
@@ -3,6 +3,10 @@
 from collections import namedtuple
 import numpy as np
 from shapely.geometry import Polygon
+"""
+reference from :
+https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8
+"""
 
 
 class DetectionIoUEvaluator(object):
diff --git a/tools/eval_utils/eval_det_utils.py b/tools/eval_utils/eval_det_utils.py
index 015cba99fb..f0be714fc0 100644
--- a/tools/eval_utils/eval_det_utils.py
+++ b/tools/eval_utils/eval_det_utils.py
@@ -98,6 +98,14 @@ def load_label_infor(label_file_path, do_ignore=False):
 
 
 def cal_det_metrics(gt_label_path, save_res_path):
+    """
+    calculate the detection metrics
+    Args:
+        gt_label_path(string): The groundtruth detection label file path
+        save_res_path(string): The saved predicted detection label path
+    return:
+        claculated metrics including Hmean、precision and recall
+    """
     evaluator = DetectionIoUEvaluator()
     gt_label_infor = load_label_infor(gt_label_path, do_ignore=True)
     dt_label_infor = load_label_infor(save_res_path)
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index e8c76de9ff..f1f7a8a0b6 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -99,15 +99,7 @@ def create_predictor(args, mode):
         config.disable_gpu()
 
     config.disable_glog_info()
-    # config.switch_ir_optim(args.ir_optim)
 
-    #     if args.use_tensorrt:
-    #         config.enable_tensorrt_engine(
-    #             precision_mode=AnalysisConfig.Precision.Half
-    #             if args.use_fp16 else AnalysisConfig.Precision.Float32,
-    #             max_batch_size=args.batch_size)
-
-    # config.enable_memory_optim()
     # use zero copy
     config.switch_use_feed_fetch_ops(False)
     predictor = create_paddle_predictor(config)
diff --git a/tools/infer_det.py b/tools/infer_det.py
index d616323d9d..7998cdb6d9 100755
--- a/tools/infer_det.py
+++ b/tools/infer_det.py
@@ -34,7 +34,7 @@ def set_paddle_flags(**kwargs):
 
 # NOTE(paddle-dev): All of these flags should be
 # set before `import paddle`. Otherwise, it would
-# not take any effect. 
+# not take any effect.
 set_paddle_flags(
     FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
 )
@@ -44,6 +44,7 @@ from ppocr.utils.utility import create_module
 import program
 from ppocr.utils.save_load import init_model
 from ppocr.data.reader_main import reader_main
+import cv2
 
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
@@ -67,6 +68,50 @@ def draw_det_res(dt_boxes, config, img_name, ino):
         logger.info("The detected Image saved in {}".format(save_path))
 
 
+def simple_reader(img_file, config):
+    imgs_lists = []
+    if img_file is None or not os.path.exists(img_file):
+        raise Exception("not found any img file in {}".format(img_file))
+
+    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
+    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+        imgs_lists.append(img_file)
+    elif os.path.isdir(img_file):
+        for single_file in os.listdir(img_file):
+            if single_file.split('.')[-1] in img_end:
+                imgs_lists.append(os.path.join(img_file, single_file))
+    if len(imgs_lists) == 0:
+        raise Exception("not found any img file in {}".format(img_file))
+
+    batch_size = config['Global']['test_batch_size_per_card']
+    global_params = config['Global']
+    params = deepcopy(config['TestReader'])
+    params.update(global_params)
+    reader_function = params['process_function']
+    process_function = create_module(reader_function)(params)
+
+    def batch_iter_reader():
+        batch_outs = []
+        for img_path in imgs_lists:
+            img = cv2.imread(img_path)
+            if img.shape[-1] == 1 or len(list(img.shape)) == 2:
+                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            if img is None:
+                logger.info("load image error:" + img_path)
+                continue
+            outs = process_function(img)
+            outs.append(os.path.basename(img_path))
+            print(outs[0].shape, outs[2])
+            batch_outs.append(outs)
+            if len(batch_outs) == batch_size:
+                yield batch_outs
+                batch_outs = []
+        if len(batch_outs) != 0:
+            yield batch_outs
+
+    return batch_iter_reader
+
+
 def main():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
@@ -103,7 +148,9 @@ def main():
 
     save_res_path = config['Global']['save_res_path']
     with open(save_res_path, "wb") as fout:
-        test_reader = reader_main(config=config, mode='test')
+        # test_reader = reader_main(config=config, mode='test')
+        single_img_path = config['TestReader']['single_img_path']
+        test_reader = simple_reader(img_file=single_img_path, config=config)
         tackling_num = 0
         for data in test_reader():
             img_num = len(data)
@@ -116,6 +163,7 @@ def main():
                 img_list.append(data[ino][0])
                 ratio_list.append(data[ino][1])
                 img_name_list.append(data[ino][2])
+
             img_list = np.concatenate(img_list, axis=0)
             outs = exe.run(eval_prog,\
                 feed={'image': img_list},\
@@ -126,7 +174,7 @@ def main():
             postprocess_params.update(global_params)
             postprocess = create_module(postprocess_params['function'])\
                 (params=postprocess_params)
-            dt_boxes_list = postprocess(outs, ratio_list)
+            dt_boxes_list = postprocess({"maps": outs[0]}, ratio_list)
             for ino in range(img_num):
                 dt_boxes = dt_boxes_list[ino]
                 img_name = img_name_list[ino]
diff --git a/tools/program.py b/tools/program.py
index a34e56ca00..f74aacc738 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -185,22 +185,6 @@ def build(config, main_prog, startup_prog, mode):
 
 def build_export(config, main_prog, startup_prog):
     """
-    Build a program using a model and an optimizer
-        1. create feeds
-        2. create a dataloader
-        3. create a model
-        4. create fetchs
-        5. create an optimizer
-
-    Args:
-        config(dict): config
-        main_prog(): main program
-        startup_prog(): startup program
-        is_train(bool): train or valid
-
-    Returns:
-        dataloader(): a bridge between the model and the data
-        fetchs(dict): dict of model outputs(included loss and measures)
     """
     with fluid.program_guard(main_prog, startup_prog):
         with fluid.unique_name.guard():

From f7bea718aa5ac2d4993d09bd36526d008092c1e7 Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Mon, 11 May 2020 21:18:39 +0800
Subject: [PATCH 4/9] update doc and requirments

---
 doc/detection.md    | 1 -
 doc/installation.md | 7 ++-----
 requirments.txt     | 3 ++-
 3 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/doc/detection.md b/doc/detection.md
index 6124b74f16..03052b7652 100644
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -55,7 +55,6 @@ python3 tools/train.py -c configs/det/det_db_mv3.yml
 python3 tools/train.py -c configs/det/det_db_mv3.yml -o Optimizer.base_lr=0.0001
 ```
 
-
 ## 3.3 指标评估
 
 PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
diff --git a/doc/installation.md b/doc/installation.md
index 5eca1dfe6a..b190b3e345 100644
--- a/doc/installation.md
+++ b/doc/installation.md
@@ -1,19 +1,16 @@
 ### 2.1 快速安装
 
-我们提供了PaddleOCR开发环境的docker，您可以pull我们提供的docker运行PaddleOCR的环境。
-
+建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
 1. 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
 # 创建一个名字为pdocr的docker容器，并将当前目录映射到容器的/data目录下
-sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it paddlepaddle/paddle:1.7.2-gpu-cuda10.0-cudnn7  /bin/bash
+sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it  hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev  /bin/bash
 ```
 
 2. 克隆PaddleOCR repo代码
 ```
-apt-get update
-apt-get install git
 git clone https://github.com/PaddlePaddle/PaddleOCR
 ```
 
diff --git a/requirments.txt b/requirments.txt
index e9bf32a993..94e8478ffa 100644
--- a/requirments.txt
+++ b/requirments.txt
@@ -1,3 +1,4 @@
 shapely
 imgaug
-pyclipper
\ No newline at end of file
+pyclipper
+lmdb
\ No newline at end of file

From 561c54460c24a0d08ee9d6c5ea4d7daf535b2aff Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Tue, 12 May 2020 15:04:33 +0800
Subject: [PATCH 5/9] update readme and doc

---
 README.md           | 126 +++++++++++++++++++++++++++++++++++++++++++-
 doc/detection.md    |   6 +--
 doc/installation.md |  14 ++++-
 3 files changed, 140 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 4d6fb7fb86..60ab666d47 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,124 @@
-# PaddleOCR
-OCR algorithms with PaddlePaddle （still under develop)
+
+# 简介
+PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。
+
+【这里加上效果图】
+
+## 文档教程
+- [快速安装](./doc/installation.md)
+- [文本识别模型训练/评估/预测](./doc/detection.md)
+- [文本预测模型训练/评估/预测](./doc/recognition.md)
+
+## 特性：
+- 超轻量级模型
+    -（检测模型4.1M + 识别模型4.5M = 8.6M）
+- 支持竖排文字
+    - (单模型同时支持横排和竖排文字识别)
+- 支持长文本识别
+- 支持中英文数字组合识别
+- 提供训练代码
+- 支持模型部署
+
+## 文本检测算法:
+
+PaddleOCR提供的文本检测算法列表：
+- [EAST](https://arxiv.org/abs/1704.03155)
+- [DB](https://arxiv.org/abs/1911.08947)
+- [SAST](https://arxiv.org/abs/1908.05498)
+
+算法效果：
+|模型|骨干网络|数据集|Hmean|
+|-|-|-|-|
+|EAST|ResNet50_vd|ICDAR2015|85.85%|
+|EAST|MobileNetV3|ICDAR2015|79.08%|
+|DB|ResNet50_vd|ICDAR2015|83.30%|
+|DB|MobileNetV3|ICDAR2015|73.00%|
+
+PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
+
+## 文本识别算法:
+
+PaddleOCR提供的文本识别算法列表：
+- (CRNN)[https://arxiv.org/abs/1507.05717]
+- [Rosetta](https://arxiv.org/abs/1910.05085)
+- [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
+- [RARE](https://arxiv.org/abs/1603.03915v1)
+- [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
+
+算法效果：
+
+以下指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均。
+
+|模型|骨干网络|ACC|
+|-|-|-|
+|Rosetta|Resnet34_vd|80.24%|
+|Rosetta|MobileNetV3|78.16%|
+|CRNN|Resnet34_vd|82.20%|
+|CRNN|MobileNetV3|79.37%|
+|STAR-Net|Resnet34_vd|83.93%|
+|STAR-Net|MobileNetV3|81.56%|
+|RARE|Resnet34_vd|84.90%|
+|RARE|MobileNetV3|83.32%|
+
+PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
+
+## 端到端算法
+PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
+- End2End-PSL (comming soon)
+
+
+
+# 参考文献
+```
+EAST:
+@inproceedings{zhou2017east,
+  title={EAST: an efficient and accurate scene text detector},
+  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
+  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  pages={5551--5560},
+  year={2017}
+}
+
+DB:
+@article{liao2019real,
+  title={Real-time Scene Text Detection with Differentiable Binarization},
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  journal={arXiv preprint arXiv:1911.08947},
+  year={2019}
+}
+
+DTRB:
+@inproceedings{baek2019wrong,
+  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
+  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={4715--4723},
+  year={2019}
+}
+
+SAST:
+@inproceedings{wang2019single,
+  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
+  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
+  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+  pages={1277--1285},
+  year={2019}
+}
+
+SRN:
+@article{yu2020towards,
+  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
+  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
+  journal={arXiv preprint arXiv:2003.12294},
+  year={2020}
+}
+
+end2end-psl:
+@inproceedings{sun2019chinese,
+  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
+  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={9086--9095},
+  year={2019}
+}
+```
diff --git a/doc/detection.md b/doc/detection.md
index 03052b7652..f4295177bc 100644
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -35,15 +35,15 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 首先下载pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
 您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
 ```
+cd PaddleOCR/
 # 下载MobileNetV3的预训练模型
-wget -P /PaddleOCR/pretrained_model/ 模型链接
+wget -P /PaddleOCR/pretrain_models/ 模型链接
 # 下载ResNet50的预训练模型
-wget -P /PaddleOCR/pretrained_model/ 模型链接
+wget -P /PaddleOCR/pretrain_models/ 模型链接
 ```
 
 **启动训练**
 ```
-cd PaddleOCR/
 python3 tools/train.py -c configs/det/det_db_mv3.yml
 ```
 
diff --git a/doc/installation.md b/doc/installation.md
index b190b3e345..5fca344ea1 100644
--- a/doc/installation.md
+++ b/doc/installation.md
@@ -1,12 +1,17 @@
-### 2.1 快速安装
+## 快速安装
 
 建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
 1. 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pdocr的docker容器，并将当前目录映射到容器的/data目录下
 sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it  hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev  /bin/bash
+
+# ctrl+P+Q可退出docker，重新进入docker使用如下命令
+sudo nvidia-docker container exec -it pdocr /bin/bash
+
 ```
 
 2. 克隆PaddleOCR repo代码
@@ -20,3 +25,10 @@ cd PaddleOCR
 pip3 install --upgrade pip
 pip3 install -r requirements.txt
 ```
+
+## 快速运行
+
+```
+python3 tools/infer/predict_eval.py --image_file="./"
+```
+【可视化运行结果】

From e91f370362c22e4ba700893a1677a0ed5abdff25 Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Tue, 12 May 2020 15:35:10 +0800
Subject: [PATCH 6/9] update readme

---
 README.md        | 11 ++++-------
 doc/detection.md |  8 ++++----
 2 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 60ab666d47..e08448f640 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,6 @@
 # 简介
 PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。
 
-【这里加上效果图】
 
 ## 文档教程
 - [快速安装](./doc/installation.md)
@@ -11,7 +10,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别
 
 ## 特性：
 - 超轻量级模型
-    -（检测模型4.1M + 识别模型4.5M = 8.6M）
+    - (检测模型4.1M + 识别模型4.5M = 8.6M)
 - 支持竖排文字
     - (单模型同时支持横排和竖排文字识别)
 - 支持长文本识别
@@ -21,7 +20,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别
 
 ## 文本检测算法:
 
-PaddleOCR提供的文本检测算法列表：
+PaddleOCR开源的文本检测算法列表：
 - [EAST](https://arxiv.org/abs/1704.03155)
 - [DB](https://arxiv.org/abs/1911.08947)
 - [SAST](https://arxiv.org/abs/1908.05498)
@@ -38,16 +37,14 @@ PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.m
 
 ## 文本识别算法:
 
-PaddleOCR提供的文本识别算法列表：
+PaddleOCR开源的文本识别算法列表：
 - (CRNN)[https://arxiv.org/abs/1507.05717]
 - [Rosetta](https://arxiv.org/abs/1910.05085)
 - [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
 - [RARE](https://arxiv.org/abs/1603.03915v1)
 - [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
 
-算法效果：
-
-以下指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均。
+算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
 
 |模型|骨干网络|ACC|
 |-|-|-|
diff --git a/doc/detection.md b/doc/detection.md
index f4295177bc..5d54d78043 100644
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -2,7 +2,7 @@
 
 本节以icdar15数据集为例，介绍PaddleOCR中检测模型的使用方式。
 
-## 3.1 数据准备
+## 数据准备
 icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
 
 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。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
@@ -30,7 +30,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 可以按照上述形式构建标注文件。
 
 
-## 3.2 快速启动训练
+## 快速启动训练
 
 首先下载pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
 您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
@@ -55,7 +55,7 @@ python3 tools/train.py -c configs/det/det_db_mv3.yml
 python3 tools/train.py -c configs/det/det_db_mv3.yml -o Optimizer.base_lr=0.0001
 ```
 
-## 3.3 指标评估
+## 指标评估
 
 PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
 
@@ -65,7 +65,7 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall
 python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints="./output/best_accuracy"
 ```
 
-## 3.4 测试检测效果
+## 测试检测效果
 
 测试单张图像的检测效果
 ```

From 5b4675e06dcd01eb44200594452d0464833da43d Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Tue, 12 May 2020 19:40:57 +0800
Subject: [PATCH 7/9] fix problems refer comments

---
 README.md                           | 56 ++++++++++++++++-------------
 doc/detection.md                    |  9 ++---
 doc/installation.md                 |  7 ----
 ppocr/data/det/dataset_traversal.py | 14 +++-----
 tools/infer/utility.py              | 25 +++++++------
 tools/infer_det.py                  | 48 +------------------------
 6 files changed, 57 insertions(+), 102 deletions(-)

diff --git a/README.md b/README.md
index e08448f640..2b889c2888 100644
--- a/README.md
+++ b/README.md
@@ -1,44 +1,49 @@
 
 # 简介
-PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。
-
-
-## 文档教程
-- [快速安装](./doc/installation.md)
-- [文本识别模型训练/评估/预测](./doc/detection.md)
-- [文本预测模型训练/评估/预测](./doc/recognition.md)
+PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
 
 ## 特性：
 - 超轻量级模型
     - (检测模型4.1M + 识别模型4.5M = 8.6M)
-- 支持竖排文字
+- 支持竖排文字识别
     - (单模型同时支持横排和竖排文字识别)
 - 支持长文本识别
 - 支持中英文数字组合识别
 - 提供训练代码
 - 支持模型部署
 
+
+## 文档教程
+- [快速安装](./doc/installation.md)
+- [快速开始]()
+- [文本识别模型训练/评估/预测](./doc/detection.md)
+- [文本预测模型训练/评估/预测](./doc/recognition.md)
+- [基于inference model预测](./doc/)
+
+
 ## 文本检测算法:
 
 PaddleOCR开源的文本检测算法列表：
-- [EAST](https://arxiv.org/abs/1704.03155)
-- [DB](https://arxiv.org/abs/1911.08947)
-- [SAST](https://arxiv.org/abs/1908.05498)
+- [x] [EAST](https://arxiv.org/abs/1704.03155)
+- [x] [DB](https://arxiv.org/abs/1911.08947)
+- [x] [SAST](https://arxiv.org/abs/1908.05498)
+- []
+
 
 算法效果：
-|模型|骨干网络|数据集|Hmean|
-|-|-|-|-|
-|EAST|ResNet50_vd|ICDAR2015|85.85%|
-|EAST|MobileNetV3|ICDAR2015|79.08%|
-|DB|ResNet50_vd|ICDAR2015|83.30%|
-|DB|MobileNetV3|ICDAR2015|73.00%|
+|模型|骨干网络|Hmean|
+|-|-|-|
+|EAST^[1]^|ResNet50_vd|85.85%|
+|EAST^[1]^|MobileNetV3|79.08%|
+|DB^[2]^|ResNet50_vd|83.30%|
+|DB^[2]^|MobileNetV3|73.00%|
 
 PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
 
 ## 文本识别算法:
 
 PaddleOCR开源的文本识别算法列表：
-- (CRNN)[https://arxiv.org/abs/1507.05717]
+- [CRNN](https://arxiv.org/abs/1507.05717)
 - [Rosetta](https://arxiv.org/abs/1910.05085)
 - [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
 - [RARE](https://arxiv.org/abs/1603.03915v1)
@@ -59,7 +64,8 @@ PaddleOCR开源的文本识别算法列表：
 
 PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
 
-## 端到端算法
+## TODO
+**端到端OCR算法**
 PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
 - End2End-PSL (comming soon)
 
@@ -67,7 +73,7 @@ PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.o
 
 # 参考文献
 ```
-EAST:
+1. EAST:
 @inproceedings{zhou2017east,
   title={EAST: an efficient and accurate scene text detector},
   author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
@@ -76,7 +82,7 @@ EAST:
   year={2017}
 }
 
-DB:
+2. DB:
 @article{liao2019real,
   title={Real-time Scene Text Detection with Differentiable Binarization},
   author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
@@ -84,7 +90,7 @@ DB:
   year={2019}
 }
 
-DTRB:
+3. DTRB:
 @inproceedings{baek2019wrong,
   title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
   author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
@@ -93,7 +99,7 @@ DTRB:
   year={2019}
 }
 
-SAST:
+4. SAST:
 @inproceedings{wang2019single,
   title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
   author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
@@ -102,7 +108,7 @@ SAST:
   year={2019}
 }
 
-SRN:
+5. SRN:
 @article{yu2020towards,
   title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
   author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
@@ -110,7 +116,7 @@ SRN:
   year={2020}
 }
 
-end2end-psl:
+6. end2end-psl:
 @inproceedings{sun2019chinese,
   title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
   author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
diff --git a/doc/detection.md b/doc/detection.md
index 5d54d78043..5e55011037 100644
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -1,6 +1,6 @@
 # 文字检测
 
-本节以icdar15数据集为例，介绍PaddleOCR中检测模型的使用方式。
+本节以icdar15数据集为例，介绍PaddleOCR中检测模型的训练、评估与测试。
 
 ## 数据准备
 icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
@@ -26,8 +26,9 @@ wget -P /PaddleOCR/train_data/  测试标注文件链接
 " 图像文件名                    json.dumps编码的图像标注信息"
 ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
 ```
-json.dumps编码前的图像标注信息是包含多个字典的list，字典中的points表示文本框的位置，如果您想在其他数据集上训练PaddleOCR,
-可以按照上述形式构建标注文件。
+json.dumps编码前的图像标注信息是包含多个字典的list，字典中的$points$表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
+$transcription$表示当前文本框的文字，在文本检测任务中并不需要这个信息。
+如果您想在其他数据集上训练PaddleOCR，可以按照上述形式构建标注文件。
 
 
 ## 快速启动训练
@@ -62,7 +63,7 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall
 运行如下代码，根据配置文件det_db_mv3.yml中save_res_path指定的测试集检测结果文件，计算评估指标。
 
 ```
-python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints="./output/best_accuracy"
+python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o Gloabl.checkpoints="./output/best_accuracy"
 ```
 
 ## 测试检测效果
diff --git a/doc/installation.md b/doc/installation.md
index 5fca344ea1..25e0d0d56c 100644
--- a/doc/installation.md
+++ b/doc/installation.md
@@ -25,10 +25,3 @@ cd PaddleOCR
 pip3 install --upgrade pip
 pip3 install -r requirements.txt
 ```
-
-## 快速运行
-
-```
-python3 tools/infer/predict_eval.py --image_file="./"
-```
-【可视化运行结果】
diff --git a/ppocr/data/det/dataset_traversal.py b/ppocr/data/det/dataset_traversal.py
index 5ba01ee790..2e68d91d56 100755
--- a/ppocr/data/det/dataset_traversal.py
+++ b/ppocr/data/det/dataset_traversal.py
@@ -22,6 +22,7 @@ import string
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.utils.utility import create_module
+from tools.infer.utility import get_image_file_list
 import time
 
 
@@ -72,16 +73,8 @@ class EvalTestReader(object):
             self.params)
         batch_size = self.params['test_batch_size_per_card']
 
-        flag_test_single_img = False
-        if mode == "test":
-            single_img_path = self.params['single_img_path']
-            if single_img_path is not None:
-                flag_test_single_img = True
-
         img_list = []
-        if flag_test_single_img:
-            img_list.append([single_img_path, single_img_path])
-        else:
+        if mode != "test":
             img_set_dir = self.params['img_set_dir']
             img_name_list_path = self.params['label_file_path']
             with open(img_name_list_path, "rb") as fin:
@@ -90,6 +83,9 @@ class EvalTestReader(object):
                     img_name = line.decode().strip("\n").split("\t")[0]
                     img_path = img_set_dir + "/" + img_name
                     img_list.append([img_path, img_name])
+        else:
+            img_path = self.params['single_img_path']
+            img_list = get_image_file_list(img_path)
 
         def batch_iter_reader():
             batch_outs = []
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index f1f7a8a0b6..01477a5c4c 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -61,16 +61,21 @@ def parse_args():
     return parser.parse_args()
 
 
-def get_image_file_list(image_dir):
-    image_file_list = []
-    if image_dir is None:
-        return image_file_list
-    if os.path.isfile(image_dir):
-        image_file_list = [image_dir]
-    elif os.path.isdir(image_dir):
-        for single_file in os.listdir(image_dir):
-            image_file_list.append(os.path.join(image_dir, single_file))
-    return image_file_list
+def get_image_file_list(img_file):
+    imgs_lists = []
+    if img_file is None or not os.path.exists(img_file):
+        raise Exception("not found any img file in {}".format(img_file))
+
+    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
+    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+        imgs_lists.append(img_file)
+    elif os.path.isdir(img_file):
+        for single_file in os.listdir(img_file):
+            if single_file.split('.')[-1] in img_end:
+                imgs_lists.append(os.path.join(img_file, single_file))
+    if len(imgs_lists) == 0:
+        raise Exception("not found any img file in {}".format(img_file))
+    return imgs_lists
 
 
 def create_predictor(args, mode):
diff --git a/tools/infer_det.py b/tools/infer_det.py
index 7998cdb6d9..8d591a654d 100755
--- a/tools/infer_det.py
+++ b/tools/infer_det.py
@@ -68,50 +68,6 @@ def draw_det_res(dt_boxes, config, img_name, ino):
         logger.info("The detected Image saved in {}".format(save_path))
 
 
-def simple_reader(img_file, config):
-    imgs_lists = []
-    if img_file is None or not os.path.exists(img_file):
-        raise Exception("not found any img file in {}".format(img_file))
-
-    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
-    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
-        imgs_lists.append(img_file)
-    elif os.path.isdir(img_file):
-        for single_file in os.listdir(img_file):
-            if single_file.split('.')[-1] in img_end:
-                imgs_lists.append(os.path.join(img_file, single_file))
-    if len(imgs_lists) == 0:
-        raise Exception("not found any img file in {}".format(img_file))
-
-    batch_size = config['Global']['test_batch_size_per_card']
-    global_params = config['Global']
-    params = deepcopy(config['TestReader'])
-    params.update(global_params)
-    reader_function = params['process_function']
-    process_function = create_module(reader_function)(params)
-
-    def batch_iter_reader():
-        batch_outs = []
-        for img_path in imgs_lists:
-            img = cv2.imread(img_path)
-            if img.shape[-1] == 1 or len(list(img.shape)) == 2:
-                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-            if img is None:
-                logger.info("load image error:" + img_path)
-                continue
-            outs = process_function(img)
-            outs.append(os.path.basename(img_path))
-            print(outs[0].shape, outs[2])
-            batch_outs.append(outs)
-            if len(batch_outs) == batch_size:
-                yield batch_outs
-                batch_outs = []
-        if len(batch_outs) != 0:
-            yield batch_outs
-
-    return batch_iter_reader
-
-
 def main():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
@@ -148,9 +104,7 @@ def main():
 
     save_res_path = config['Global']['save_res_path']
     with open(save_res_path, "wb") as fout:
-        # test_reader = reader_main(config=config, mode='test')
-        single_img_path = config['TestReader']['single_img_path']
-        test_reader = simple_reader(img_file=single_img_path, config=config)
+        test_reader = reader_main(config=config, mode='test')
         tackling_num = 0
         for data in test_reader():
             img_num = len(data)

From b2e2bb98fb8a46898723281e0e69b18d0e7286dc Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Tue, 12 May 2020 21:12:52 +0800
Subject: [PATCH 8/9] fix problems responding to inference

---
 README.md                           | 54 +++++++++++++++++++--------
 configs/det/det_db_mv3.yml          |  1 +
 doc/inference.md                    | 58 +++++++++++++++++++++++++++++
 ppocr/data/det/dataset_traversal.py |  2 +-
 ppocr/utils/utility.py              | 18 +++++++++
 tools/export_model.py               |  7 +++-
 tools/infer/utility.py              | 17 ---------
 7 files changed, 123 insertions(+), 34 deletions(-)
 create mode 100644 doc/inference.md

diff --git a/README.md b/README.md
index 2b889c2888..6a9adfa608 100644
--- a/README.md
+++ b/README.md
@@ -15,39 +15,63 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 ## 文档教程
 - [快速安装](./doc/installation.md)
-- [快速开始]()
 - [文本识别模型训练/评估/预测](./doc/detection.md)
 - [文本预测模型训练/评估/预测](./doc/recognition.md)
 - [基于inference model预测](./doc/)
 
+### **快速开始**
+
+下载inference模型
+```
+# 创建inference模型保存目录
+mkdir inference && cd inference && mkdir det && mkdir rec
+# 下载检测inference模型
+wget -P ./inference/det 检测inference模型链接
+# 下载识别inference模型
+wget -P ./inferencee/rec 识别inference模型链接
+```
+
+实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
+```
+export PYTHONPATH=.
+python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
+
+实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
+```
+python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+
+
 
 ## 文本检测算法:
 
 PaddleOCR开源的文本检测算法列表：
-- [x] [EAST](https://arxiv.org/abs/1704.03155)
-- [x] [DB](https://arxiv.org/abs/1911.08947)
-- [x] [SAST](https://arxiv.org/abs/1908.05498)
-- []
+- [x]  [EAST](https://arxiv.org/abs/1704.03155)
+- [x]  [DB](https://arxiv.org/abs/1911.08947)
+- [ ]  [SAST](https://arxiv.org/abs/1908.05498)
 
 
 算法效果：
 |模型|骨干网络|Hmean|
 |-|-|-|
-|EAST^[1]^|ResNet50_vd|85.85%|
-|EAST^[1]^|MobileNetV3|79.08%|
-|DB^[2]^|ResNet50_vd|83.30%|
-|DB^[2]^|MobileNetV3|73.00%|
+|EAST|ResNet50_vd|85.85%|
+|EAST|MobileNetV3|79.08%|
+|DB|ResNet50_vd|83.30%|
+|DB|MobileNetV3|73.00%|
 
 PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
 
 ## 文本识别算法:
 
 PaddleOCR开源的文本识别算法列表：
-- [CRNN](https://arxiv.org/abs/1507.05717)
-- [Rosetta](https://arxiv.org/abs/1910.05085)
-- [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
-- [RARE](https://arxiv.org/abs/1603.03915v1)
-- [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
+- [x]  [CRNN](https://arxiv.org/abs/1507.05717)
+- [x]  [DTRB](https://arxiv.org/abs/1904.01906)
+- [ ]  [Rosetta](https://arxiv.org/abs/1910.05085)
+- [ ]  [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
+- [ ]  [RARE](https://arxiv.org/abs/1603.03915v1)
+- [ ]  [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
 
 算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
 
@@ -67,7 +91,7 @@ PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition
 ## TODO
 **端到端OCR算法**
 PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
-- End2End-PSL (comming soon)
+- [ ]  End2End-PSL (comming soon)
 
 
 
diff --git a/configs/det/det_db_mv3.yml b/configs/det/det_db_mv3.yml
index 197b12044b..45e2ee17b7 100755
--- a/configs/det/det_db_mv3.yml
+++ b/configs/det/det_db_mv3.yml
@@ -14,6 +14,7 @@ Global:
   pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
   checkpoints:
   save_res_path: ./output/predicts_db.txt
+  save_inference_dir:
   
 Architecture:
   function: ppocr.modeling.architectures.det_model,DetModel
diff --git a/doc/inference.md b/doc/inference.md
new file mode 100644
index 0000000000..d44a96d7e2
--- /dev/null
+++ b/doc/inference.md
@@ -0,0 +1,58 @@
+
+# 基于inference model的推理
+
+inference 模型（fluid.io.save_inference_model保存的模型）
+一般是模型训练完成后保存的固化模型，多用于预测部署。
+训练过程中保存的模型是checkpoints模型，保存的是模型的参数，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。
+
+PaddleOCR提供了将checkpoints转换成inference model的实现。
+
+
+## 文本检测模型推理
+
+将文本检测模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
+
+```
+python tools/export_model.py -c configs/det/det_db_mv3.yml -o Global.checkpoints="./output/best_accuracy" \
+        Global.save_inference_dir="./inference/det/"
+```
+
+推理模型保存在$./inference/det/model$, $./inference/det/params$
+
+使用保存的inference model实现在单张图像上的预测：
+
+```
+python  tools/infer/predict_det.py --image_dir="/demo.jpg" --det_model_dir="./inference/det/"
+```
+
+
+## 文本识别模型推理
+
+将文本识别模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
+
+```
+python tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints="./output/best_accuracy" \
+        Global.save_inference_dir="./inference/rec/"
+```
+
+推理模型保存在$./inference/rec/model$, $./inference/rec/params$
+
+使用保存的inference model实现在单张图像上的预测：
+
+```
+python  tools/infer/predict_rec.py --image_dir="/demo.jpg" --rec_model_dir="./inference/rec/"
+```
+
+## 文本检测、识别串联推理
+
+实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
+```
+python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+
+实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
+
+```
+python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
diff --git a/ppocr/data/det/dataset_traversal.py b/ppocr/data/det/dataset_traversal.py
index 2e68d91d56..0feedeeb65 100755
--- a/ppocr/data/det/dataset_traversal.py
+++ b/ppocr/data/det/dataset_traversal.py
@@ -22,7 +22,7 @@ import string
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.utils.utility import create_module
-from tools.infer.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list
 import time
 
 
diff --git a/ppocr/utils/utility.py b/ppocr/utils/utility.py
index 6a81465c1f..c824d4404a 100755
--- a/ppocr/utils/utility.py
+++ b/ppocr/utils/utility.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import logging
+import os
 
 
 def initial_logger():
@@ -55,6 +56,23 @@ def get_check_reader_params(mode):
     return check_params
 
 
+def get_image_file_list(img_file):
+    imgs_lists = []
+    if img_file is None or not os.path.exists(img_file):
+        raise Exception("not found any img file in {}".format(img_file))
+
+    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
+    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+        imgs_lists.append(img_file)
+    elif os.path.isdir(img_file):
+        for single_file in os.listdir(img_file):
+            if single_file.split('.')[-1] in img_end:
+                imgs_lists.append(os.path.join(img_file, single_file))
+    if len(imgs_lists) == 0:
+        raise Exception("not found any img file in {}".format(img_file))
+    return imgs_lists
+
+
 from paddle import fluid
 
 
diff --git a/tools/export_model.py b/tools/export_model.py
index 6c924f3fb2..11a2744b39 100644
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -71,14 +71,19 @@ def main():
 
     init_model(config, eval_program, exe)
 
+    save_inference_dir = config['Global']['save_inference_dir']
+    if not os.path.exists(save_inference_dir):
+        os.makedirs(save_inference_dir)
     fluid.io.save_inference_model(
-        dirname="./output/",
+        dirname=save_inference_dir,
         feeded_var_names=feeded_var_names,
         main_program=eval_program,
         target_vars=target_vars,
         executor=exe,
         model_filename='model',
         params_filename='params')
+    print("inference model saved in {}/model and {}/params".format(
+        save_inference_dir, save_inference_dir))
     print("save success, output_name_list:", fetches_var_name)
 
 
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 01477a5c4c..6d56a99080 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -61,23 +61,6 @@ def parse_args():
     return parser.parse_args()
 
 
-def get_image_file_list(img_file):
-    imgs_lists = []
-    if img_file is None or not os.path.exists(img_file):
-        raise Exception("not found any img file in {}".format(img_file))
-
-    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
-    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
-        imgs_lists.append(img_file)
-    elif os.path.isdir(img_file):
-        for single_file in os.listdir(img_file):
-            if single_file.split('.')[-1] in img_end:
-                imgs_lists.append(os.path.join(img_file, single_file))
-    if len(imgs_lists) == 0:
-        raise Exception("not found any img file in {}".format(img_file))
-    return imgs_lists
-
-
 def create_predictor(args, mode):
     if mode == "det":
         model_dir = args.det_model_dir

From ab8fd7d9477840a1d0e3ca63dc97f365776a26a6 Mon Sep 17 00:00:00 2001
From: LDOUBLEV <liuvv0203@outlook.com>
Date: Tue, 12 May 2020 21:26:28 +0800
Subject: [PATCH 9/9] remove README for now

---
 README.md     | 153 +-------------------------------------------------
 doc/README.md | 151 +++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 153 insertions(+), 151 deletions(-)
 create mode 100644 doc/README.md

diff --git a/README.md b/README.md
index 6a9adfa608..4d6fb7fb86 100644
--- a/README.md
+++ b/README.md
@@ -1,151 +1,2 @@
-
-# 简介
-PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
-
-## 特性：
-- 超轻量级模型
-    - (检测模型4.1M + 识别模型4.5M = 8.6M)
-- 支持竖排文字识别
-    - (单模型同时支持横排和竖排文字识别)
-- 支持长文本识别
-- 支持中英文数字组合识别
-- 提供训练代码
-- 支持模型部署
-
-
-## 文档教程
-- [快速安装](./doc/installation.md)
-- [文本识别模型训练/评估/预测](./doc/detection.md)
-- [文本预测模型训练/评估/预测](./doc/recognition.md)
-- [基于inference model预测](./doc/)
-
-### **快速开始**
-
-下载inference模型
-```
-# 创建inference模型保存目录
-mkdir inference && cd inference && mkdir det && mkdir rec
-# 下载检测inference模型
-wget -P ./inference/det 检测inference模型链接
-# 下载识别inference模型
-wget -P ./inferencee/rec 识别inference模型链接
-```
-
-实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
-```
-export PYTHONPATH=.
-python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
-```
-在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
-
-实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
-```
-python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
-```
-
-
-
-## 文本检测算法:
-
-PaddleOCR开源的文本检测算法列表：
-- [x]  [EAST](https://arxiv.org/abs/1704.03155)
-- [x]  [DB](https://arxiv.org/abs/1911.08947)
-- [ ]  [SAST](https://arxiv.org/abs/1908.05498)
-
-
-算法效果：
-|模型|骨干网络|Hmean|
-|-|-|-|
-|EAST|ResNet50_vd|85.85%|
-|EAST|MobileNetV3|79.08%|
-|DB|ResNet50_vd|83.30%|
-|DB|MobileNetV3|73.00%|
-
-PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
-
-## 文本识别算法:
-
-PaddleOCR开源的文本识别算法列表：
-- [x]  [CRNN](https://arxiv.org/abs/1507.05717)
-- [x]  [DTRB](https://arxiv.org/abs/1904.01906)
-- [ ]  [Rosetta](https://arxiv.org/abs/1910.05085)
-- [ ]  [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
-- [ ]  [RARE](https://arxiv.org/abs/1603.03915v1)
-- [ ]  [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
-
-算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
-
-|模型|骨干网络|ACC|
-|-|-|-|
-|Rosetta|Resnet34_vd|80.24%|
-|Rosetta|MobileNetV3|78.16%|
-|CRNN|Resnet34_vd|82.20%|
-|CRNN|MobileNetV3|79.37%|
-|STAR-Net|Resnet34_vd|83.93%|
-|STAR-Net|MobileNetV3|81.56%|
-|RARE|Resnet34_vd|84.90%|
-|RARE|MobileNetV3|83.32%|
-
-PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
-
-## TODO
-**端到端OCR算法**
-PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
-- [ ]  End2End-PSL (comming soon)
-
-
-
-# 参考文献
-```
-1. EAST:
-@inproceedings{zhou2017east,
-  title={EAST: an efficient and accurate scene text detector},
-  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
-  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
-  pages={5551--5560},
-  year={2017}
-}
-
-2. DB:
-@article{liao2019real,
-  title={Real-time Scene Text Detection with Differentiable Binarization},
-  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
-  journal={arXiv preprint arXiv:1911.08947},
-  year={2019}
-}
-
-3. DTRB:
-@inproceedings{baek2019wrong,
-  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
-  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={4715--4723},
-  year={2019}
-}
-
-4. SAST:
-@inproceedings{wang2019single,
-  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
-  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
-  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
-  pages={1277--1285},
-  year={2019}
-}
-
-5. SRN:
-@article{yu2020towards,
-  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
-  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
-  journal={arXiv preprint arXiv:2003.12294},
-  year={2020}
-}
-
-6. end2end-psl:
-@inproceedings{sun2019chinese,
-  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
-  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={9086--9095},
-  year={2019}
-}
-```
+# PaddleOCR
+OCR algorithms with PaddlePaddle （still under develop)
diff --git a/doc/README.md b/doc/README.md
new file mode 100644
index 0000000000..6a9adfa608
--- /dev/null
+++ b/doc/README.md
@@ -0,0 +1,151 @@
+
+# 简介
+PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
+
+## 特性：
+- 超轻量级模型
+    - (检测模型4.1M + 识别模型4.5M = 8.6M)
+- 支持竖排文字识别
+    - (单模型同时支持横排和竖排文字识别)
+- 支持长文本识别
+- 支持中英文数字组合识别
+- 提供训练代码
+- 支持模型部署
+
+
+## 文档教程
+- [快速安装](./doc/installation.md)
+- [文本识别模型训练/评估/预测](./doc/detection.md)
+- [文本预测模型训练/评估/预测](./doc/recognition.md)
+- [基于inference model预测](./doc/)
+
+### **快速开始**
+
+下载inference模型
+```
+# 创建inference模型保存目录
+mkdir inference && cd inference && mkdir det && mkdir rec
+# 下载检测inference模型
+wget -P ./inference/det 检测inference模型链接
+# 下载识别inference模型
+wget -P ./inferencee/rec 识别inference模型链接
+```
+
+实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
+```
+export PYTHONPATH=.
+python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
+
+实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
+```
+python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+
+
+
+## 文本检测算法:
+
+PaddleOCR开源的文本检测算法列表：
+- [x]  [EAST](https://arxiv.org/abs/1704.03155)
+- [x]  [DB](https://arxiv.org/abs/1911.08947)
+- [ ]  [SAST](https://arxiv.org/abs/1908.05498)
+
+
+算法效果：
+|模型|骨干网络|Hmean|
+|-|-|-|
+|EAST|ResNet50_vd|85.85%|
+|EAST|MobileNetV3|79.08%|
+|DB|ResNet50_vd|83.30%|
+|DB|MobileNetV3|73.00%|
+
+PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
+
+## 文本识别算法:
+
+PaddleOCR开源的文本识别算法列表：
+- [x]  [CRNN](https://arxiv.org/abs/1507.05717)
+- [x]  [DTRB](https://arxiv.org/abs/1904.01906)
+- [ ]  [Rosetta](https://arxiv.org/abs/1910.05085)
+- [ ]  [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
+- [ ]  [RARE](https://arxiv.org/abs/1603.03915v1)
+- [ ]  [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
+
+算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
+
+|模型|骨干网络|ACC|
+|-|-|-|
+|Rosetta|Resnet34_vd|80.24%|
+|Rosetta|MobileNetV3|78.16%|
+|CRNN|Resnet34_vd|82.20%|
+|CRNN|MobileNetV3|79.37%|
+|STAR-Net|Resnet34_vd|83.93%|
+|STAR-Net|MobileNetV3|81.56%|
+|RARE|Resnet34_vd|84.90%|
+|RARE|MobileNetV3|83.32%|
+
+PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
+
+## TODO
+**端到端OCR算法**
+PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
+- [ ]  End2End-PSL (comming soon)
+
+
+
+# 参考文献
+```
+1. EAST:
+@inproceedings{zhou2017east,
+  title={EAST: an efficient and accurate scene text detector},
+  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
+  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  pages={5551--5560},
+  year={2017}
+}
+
+2. DB:
+@article{liao2019real,
+  title={Real-time Scene Text Detection with Differentiable Binarization},
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  journal={arXiv preprint arXiv:1911.08947},
+  year={2019}
+}
+
+3. DTRB:
+@inproceedings{baek2019wrong,
+  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
+  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={4715--4723},
+  year={2019}
+}
+
+4. SAST:
+@inproceedings{wang2019single,
+  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
+  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
+  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+  pages={1277--1285},
+  year={2019}
+}
+
+5. SRN:
+@article{yu2020towards,
+  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
+  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
+  journal={arXiv preprint arXiv:2003.12294},
+  year={2020}
+}
+
+6. end2end-psl:
+@inproceedings{sun2019chinese,
+  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
+  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={9086--9095},
+  year={2019}
+}
+```