Merge pull request #5 from PaddlePaddle/develop

merge paddleocr

deploy/lite/readme_en.md

@@ -13,7 +13,7 @@ deployment solutions for end-side deployment issues.
 - Computer (for Compiling Paddle Lite)
 - Mobile phone (arm7 or arm8)
 
-## 2. Build ncnn library
+## 2. Build PaddleLite library
 [build for Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
 [build for Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
 [build for MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)

deploy/pdserving/det_local_server.py

@@ -23,7 +23,7 @@ from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes
 if sys.argv[1] == 'gpu':
     from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu'
+elif sys.argv[1] == 'cpu':
     from paddle_serving_server.web_service import WebService
 import time
 import re
@@ -67,11 +67,13 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_det_model")
+ocr_service.init_det()
 if sys.argv[1] == 'gpu':
     ocr_service.set_gpus("0")
     ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+    ocr_service.run_debugger_service(gpu=True)
 elif sys.argv[1] == 'cpu':
     ocr_service.prepare_server(workdir="workdir", port=9292)
+    ocr_service.run_debugger_service()
 ocr_service.init_det()
-ocr_service.run_debugger_service()
 ocr_service.run_web_service()

deploy/pdserving/ocr_local_server.py

@@ -104,10 +104,11 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_rec_model")
-ocr_service.prepare_server(workdir="workdir", port=9292)
 ocr_service.init_det_debugger(det_model_config="ocr_det_model")
 if sys.argv[1] == 'gpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
     ocr_service.run_debugger_service(gpu=True)
 elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
     ocr_service.run_debugger_service()
 ocr_service.run_web_service()

deploy/pdserving/readme.md

@@ -55,6 +55,23 @@ tar -xzvf ocr_det.tar.gz
 ```
 执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。
 
+我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在：
+
+```
+wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
+tar xf ch_rec_r34_vd_crnn_infer.tar
+```
+因此我们按照Serving模型转换教程，运行下列python文件。
+```
+from paddle_serving_client.io import inference_model_to_serving
+inference_model_dir = "ch_rec_r34_vd_crnn"
+serving_client_dir = "serving_client_dir"
+serving_server_dir = "serving_server_dir"
+feed_var_names, fetch_var_names = inference_model_to_serving(
+        inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
+```
+最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。
+
 ### 3. 启动服务
 启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：  
 

deploy/pdserving/rec_local_server.py

@@ -22,7 +22,10 @@ from paddle_serving_client import Client
 from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-from paddle_serving_server_gpu.web_service import WebService
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
@@ -65,8 +68,12 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_rec_model")
-ocr_service.set_gpus("0")
 ocr_service.init_rec()
-ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-ocr_service.run_debugger_service()
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+    ocr_service.run_debugger_service(gpu=True)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_debugger_service()
 ocr_service.run_web_service()

doc/doc_ch/config.md

@@ -63,8 +63,9 @@
 |         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         |               \             |
 |         decay           |         是否使用decay       |    \              |               \             |
-|      function(decay)    |         设置decay方式       |   -    |       目前支持cosine_decay与piecewise_decay  |
-|      step_each_epoch    |      每个epoch包含多少次迭代, cosine_decay时有效   |         20       | 计算方式：total_image_num / (batch_size_per_card * card_size) |
-|        total_epoch      |    总共迭代多少个epoch, cosine_decay时有效        |       1000      | 与Global.epoch_num 一致        |
+|      function(decay)    |         设置decay方式       |   -    |       目前支持cosine_decay, cosine_decay_warmup与piecewise_decay  |
+|      step_each_epoch    |      每个epoch包含多少次迭代, cosine_decay/cosine_decay_warmup时有效   |         20       | 计算方式：total_image_num / (batch_size_per_card * card_size) |
+|        total_epoch      |    总共迭代多少个epoch, cosine_decay/cosine_decay_warmup时有效        |       1000      | 与Global.epoch_num 一致        |
+|        warmup_minibatch      |  线性warmup的迭代次数, cosine_decay_warmup时有效        |       1000      | \        |
 |        boundaries      |    学习率下降时的迭代次数间隔, piecewise_decay时有效       |       -      | 参数为列表形式        |
 |        decay_rate      |    学习率衰减系数, piecewise_decay时有效       |       -      |  \        |

doc/doc_ch/detection.md

 # 文字检测
 
-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。
+本节以icdar2015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。
 
 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。
-```
+```shell
 # 在PaddleOCR路径下
 cd PaddleOCR/
 wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
@@ -23,21 +23,21 @@ wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_la
   └─ test_icdar2015_label.txt     icdar数据集的测试标注
 ```
 
-提供的标注文件格式为，其中中间是"\t"分隔：
+提供的标注文件格式如下，中间用"\t"分隔：
 ```
 " 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。
+`transcription` 表示当前文本框的文字，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
 
+如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
 
 ## 快速启动训练
 
 首先下载模型backbone的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。
-```
+```shell
 cd PaddleOCR/
 # 下载MobileNetV3的预训练模型
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
@@ -45,7 +45,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 
 # 解压预训练模型文件，以MobileNetV3为例
-tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
+tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
 
 # 注：正确解压backbone预训练权重文件后，文件夹下包含众多以网络层命名的权重文件，格式如下：
 ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
@@ -57,11 +57,11 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
 
 ```
 
-**启动训练**
+#### 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*
 
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
 ```
 
@@ -69,52 +69,52 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=
 有关配置文件的详细解释，请参考[链接](./config.md)。
 
 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
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
 
-**断点训练**
+#### 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/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
 ```
 
-**注意**：Global.checkpoints的优先级高于Global.pretrain_weights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.pretrain_weights指定的模型。
+**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`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.pretrain_weights`指定的模型。
 
 ## 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。
+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。
 
-评估时设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
-```
+评估时设置后处理参数`box_thresh=0.6`，`unclip_ratio=1.5`，使用不同数据集、不同模型训练，可调整这两个参数进行优化
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
-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。
+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。
 
 比如：
-```
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
-* 注：box_thresh、unclip_ratio是DB后处理所需要的参数，在评估EAST模型时不需要设置
+* 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
 
 ## 测试检测效果
 
 测试单张图像的检测效果
-```
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
 ```
 
 测试DB模型时，调整后处理阈值，
-```
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 
 测试文件夹下所有图像的检测效果
-```
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
 ```

doc/doc_ch/inference.md

 
 # 基于Python预测引擎推理
 
-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。
+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，灵活方便，适合与实际系统集成。更详细的介绍请参考文档[分类预测框架](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
 
 接下来首先介绍如何将训练的模型转换成inference模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。
 
+
+- [一、训练模型转inference模型](#训练模型转inference模型)
+    - [检测模型转inference模型](#检测模型转inference模型)
+    - [识别模型转inference模型](#识别模型转inference模型)  
+    
+- [二、文本检测模型推理](#文本检测模型推理)
+    - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
+    - [2. DB文本检测模型推理](#DB文本检测模型推理)
+    - [3. EAST文本检测模型推理](#EAST文本检测模型推理)
+    - [4. SAST文本检测模型推理](#SAST文本检测模型推理)  
+    
+- [三、文本识别模型推理](#文本识别模型推理)
+    - [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
+    - [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
+    - [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
+    - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)  
+    
+- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
+    - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
+    - [2. 其他模型推理](#其他模型推理)
+    
+    
+<a name="训练模型转inference模型"></a>
 ## 一、训练模型转inference模型
+<a name="检测模型转inference模型"></a>
 ### 检测模型转inference模型
 
 下载超轻量级中文检测模型：
@@ -24,15 +47,16 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar &
 
 python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
 ```
-转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的Global.checkpoints、Global.save_inference_dir参数。
-其中Global.checkpoints指向训练中保存的模型参数文件，Global.save_inference_dir是生成的inference模型要保存的目录。
-转换成功后，在save_inference_dir 目录下有两个文件：
+转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`、`Global.save_inference_dir`参数。
+其中`Global.checkpoints`指向训练中保存的模型参数文件，`Global.save_inference_dir`是生成的inference模型要保存的目录。
+转换成功后，在`save_inference_dir`目录下有两个文件：
 ```
 inference/det_db/
   └─  model     检测inference模型的program文件
   └─  params    检测inference模型的参数文件
 ```
 
+<a name="识别模型转inference模型"></a>
 ### 识别模型转inference模型
 
 下载超轻量中文识别模型：
@@ -51,7 +75,7 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
         Global.save_inference_dir=./inference/rec_crnn/
 ```
 
-如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的character_dict_path是否是所需要的字典文件。
+**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
 
 转换成功后，在目录下有两个文件：
 ```
@@ -60,11 +84,13 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
   └─  params    识别inference模型的参数文件
 ```
 
+<a name="文本检测模型推理"></a>
 ## 二、文本检测模型推理
 
-下面将介绍超轻量中文检测模型推理、DB文本检测模型推理和EAST文本检测模型推理。默认配置是根据DB文本检测模型推理设置的。由于EAST和DB算法差别很大，在推理时，需要通过传入相应的参数适配EAST文本检测算法。
+文本检测模型推理，默认使用DB模型的配置参数。当不使用DB模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
 
-### 1.超轻量中文检测模型推理
+<a name="超轻量中文检测模型推理"></a>
+### 1. 超轻量中文检测模型推理
 
 超轻量中文检测模型推理，可以执行如下命令：
 
@@ -72,11 +98,11 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"
 ```
 
-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：
+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：
 
 ![](../imgs_results/det_res_2.jpg)
 
-通过设置参数det_max_side_len的大小，改变检测算法中图片规范化的最大值。当图片的长宽都小于det_max_side_len，则使用原图预测，否则将图片等比例缩放到最大值，进行预测。该参数默认设置为det_max_side_len=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，可以执行如下命令：
+通过设置参数`det_max_side_len`的大小，改变检测算法中图片规范化的最大值。当图片的长宽都小于`det_max_side_len`，则使用原图预测，否则将图片等比例缩放到最大值，进行预测。该参数默认设置为`det_max_side_len=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，可以执行如下命令：
 
 ```
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200
@@ -87,7 +113,8 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
 ```
 
-### 2.DB文本检测模型推理
+<a name="DB文本检测模型推理"></a>
+### 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，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar))，可以使用如下命令进行转换：
 
@@ -105,13 +132,14 @@ DB文本检测模型推理，可以执行如下命令：
 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：
+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：
 
 ![](../imgs_results/det_res_img_10_db.jpg)
 
-**注意**：由于ICDAR2015数据集只有1000张训练图像，主要针对英文场景，所以上述模型对中文文本图像检测效果非常差。
+**注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。
 
-### 3.EAST文本检测模型推理
+<a name="EAST文本检测模型推理"></a>
+### 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，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar))，可以使用如下命令进行转换：
 
@@ -123,24 +151,59 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
 ```
 
-EAST文本检测模型推理，需要设置参数det_algorithm，指定检测算法类型为EAST，可以执行如下命令：
+**EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令：
 
 ```
 python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/"
 ```
-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：
+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：
 
 ![](../imgs_results/det_res_img_10_east.jpg)
 
-**注意**：本代码库中EAST后处理中NMS采用的Python版本，所以预测速度比较耗时。如果采用C++版本，会有明显加速。
+**注意**：本代码库中，EAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
+
+
+<a name="SAST文本检测模型推理"></a>
+### 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，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar))，可以使用如下命令进行转换：
+```
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15"
+```
+**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
+```
+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：
+
+![](../imgs_results/det_res_img_10_sast.jpg)
+
+#### (2). 弯曲文本检测模型（Total-Text）  
+首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar))，可以使用如下命令进行转换：
+
+```
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt"
+```
+
+**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，**可以执行如下命令：
+```
+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：
 
+![](../imgs_results/det_res_img623_sast.jpg)
 
+**注意**：本代码库中，SAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
+
+
+<a name="文本识别模型推理"></a>
 ## 三、文本识别模型推理
 
 下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。
 
 
-### 1.超轻量中文识别模型推理
+<a name="超轻量中文识别模型推理"></a>
+### 1. 超轻量中文识别模型推理
 
 超轻量中文识别模型推理，可以执行如下命令：
 
@@ -155,7 +218,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
 Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
 
 
-### 2.基于CTC损失的识别模型推理
+<a name="基于CTC损失的识别模型推理"></a>
+### 2. 基于CTC损失的识别模型推理
 
 我们以STAR-Net为例，介绍基于CTC损失的识别模型推理。 CRNN和Rosetta使用方式类似，不用设置识别算法参数rec_algorithm。
 
@@ -176,7 +240,8 @@ STAR-Net文本识别模型推理，可以执行如下命令：
 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_type="en"
 ```
 
-### 3.基于Attention损失的识别模型推理
+<a name="基于Attention损失的识别模型推理"></a>
+### 3. 基于Attention损失的识别模型推理
 
 基于Attention损失的识别模型与ctc不同，需要额外设置识别算法参数 --rec_algorithm="RARE"
 
@@ -202,16 +267,18 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
 
-### 4.自定义文本识别字典的推理
+<a name="自定义文本识别字典的推理"></a>
+### 4. 自定义文本识别字典的推理
 如果训练时修改了文本的字典，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
 
 ```
 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_type="en" --rec_char_dict_path="your text dict path"
 ```
 
+<a name="文本检测、识别串联推理"></a>
 ## 四、文本检测、识别串联推理
-
-### 1.超轻量中文OCR模型推理
+<a name="超轻量中文OCR模型推理"></a>
+### 1. 超轻量中文OCR模型推理
 
 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。
 
@@ -223,9 +290,14 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model
 
 ![](../imgs_results/2.jpg)
 
-### 2.其他模型推理
+<a name="其他模型推理"></a>
+### 2. 其他模型推理
+
+如果想尝试使用其他检测算法或者识别算法，请参考上述文本检测模型推理和文本识别模型推理，更新相应配置和模型。
+
+**注意：由于检测框矫正逻辑的局限性，暂不支持使用SAST弯曲文本检测模型（即，使用参数`--det_sast_polygon=True`时）进行模型串联。**
 
-如果想尝试使用其他检测算法或者识别算法，请参考上述文本检测模型推理和文本识别模型推理，更新相应配置和模型，下面给出基于EAST文本检测和STAR-Net文本识别执行命令：
+下面给出基于EAST文本检测和STAR-Net文本识别执行命令：
 
 ```
 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_type="en"

doc/doc_ch/quickstart.md

@@ -5,6 +5,8 @@
 
 请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
 
+*注意：也可以通过 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)。*
+
 ## 2.inference模型下载
 
 |模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|

doc/doc_ch/recognition.md

@@ -18,6 +18,8 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 
 若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
 
+如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
+
 * 使用自己数据集：
 
 若您希望使用自己的数据进行训练，请参考下文组织您的数据。
@@ -161,6 +163,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
 | rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
 | rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
 | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
+| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
 
 训练中文数据，推荐使用`rec_chinese_lite_train.yml`，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
 

doc/doc_ch/update.md

 # 更新
+- 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，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
+- 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

doc/doc_ch/whl.md

+# paddleocr package使用说明
+
+## 快速上手
+
+### 安装whl包
+
+pip安装
+```bash
+pip install paddleocr
+```
+
+本地构建并安装
+```bash
+python setup.py bdist_wheel
+pip install dist/paddleocr-0.0.3-py3-none-any.whl
+```
+### 1. 代码使用
+
+* 检测+识别全流程
+```python
+from paddleocr import PaddleOCR, draw_ocr
+ocr = PaddleOCR() # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs/11.jpg'
+result = ocr.ocr(img_path)
+for line in result:
+    print(line)
+
+# 显示结果
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+结果是一个list，每个item包含了文本框，文字和识别置信度
+```bash
+[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
+[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
+[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
+......
+```
+结果可视化
+
+<div align="center">
+    <img src="../imgs_results/whl/11_det_rec.jpg" width="800">
+</div>
+
+* 单独执行检测
+```python
+from paddleocr import PaddleOCR, draw_ocr
+ocr = PaddleOCR() # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs/11.jpg'
+result = ocr.ocr(img_path,rec=False)
+for line in result:
+    print(line)
+
+# 显示结果
+from PIL import Image
+
+image = Image.open(img_path).convert('RGB')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+结果是一个list，每个item只包含文本框
+```bash
+[[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]]
+......
+```
+结果可视化
+
+
+<div align="center">
+    <img src="../imgs_results/whl/11_det.jpg" width="800">
+</div>
+
+* 单独执行识别
+```python
+from paddleocr import PaddleOCR
+ocr = PaddleOCR() # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
+result = ocr.ocr(img_path,det=False)
+for line in result:
+    print(line)
+```
+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', 0.9907421]
+```
+
+### 通过命令行使用
+
+查看帮助信息
+```bash
+paddleocr -h
+```
+
+* 检测+识别全流程
+```bash
+paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
+```
+结果是一个list，每个item包含了文本框，文字和识别置信度
+```bash
+[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
+[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
+[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
+......
+```
+
+* 单独执行检测
+```bash
+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
+```bash
+[[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]]
+......
+```
+
+* 单独执行识别
+```bash
+paddleocr --image_dir PaddleOCR/doc/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', 0.9907421]
+```
+
+## 自定义模型
+当内置模型无法满足需求时，需要使用到自己训练的模型。
+首先，参照[inference.md](./inference.md) 第一节转换将检测和识别模型转换为inference模型，然后按照如下方式使用
+
+### 代码使用
+```python
+from paddleocr import PaddleOCR, draw_ocr
+# 检测模型和识别模型路径下必须含有model和params文件
+ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}')
+img_path = 'PaddleOCR/doc/imgs/11.jpg'
+result = ocr.ocr(img_path)
+for line in result:
+    print(line)
+
+# 显示结果
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+### 通过命令行使用
+
+```bash
+paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
+```
+
+## 参数说明
+
+| 字段                    | 说明                                                                                                                                                                                                                 | 默认值                  |
+|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
+| use_gpu                 | 是否使用GPU                                                                                                                                                                                                          | TRUE                    |
+| gpu_mem                 | 初始化占用的GPU内存大小                                                                                                                                                                                              | 8000M                   |
+| image_dir               | 通过命令行调用时执行预测的图片或文件夹路径                                                                                                                                                                           |                         |
+| det_algorithm           | 使用的检测算法类型                                                                                                                                                                                                   | DB                      |
+| det_model_dir          |  检测模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/det`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 |   None        |
+| det_max_side_len        | 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放                                                                                                                         | 960                     |
+| det_db_thresh           | DB模型输出预测图的二值化阈值                                                                                                                                                                                         | 0.3                     |
+| det_db_box_thresh       | DB模型输出框的阈值，低于此值的预测框会被丢弃                                                                                                                                                                           | 0.5                     |
+| det_db_unclip_ratio     | DB模型输出框扩大的比例                                                                                                                                                                                               | 2                       |
+| det_east_score_thresh   | EAST模型输出预测图的二值化阈值                                                                                                                                                                                       | 0.8                     |
+| det_east_cover_thresh   | EAST模型输出框的阈值，低于此值的预测框会被丢弃                                                                                                                                                                         | 0.1                     |
+| det_east_nms_thresh     | EAST模型输出框NMS的阈值                                                                                                                                                                                              | 0.2                     |
+| rec_algorithm           | 使用的识别算法类型                                                                                                                                                                                                   | CRNN                    |
+| rec_model_dir          | 识别模型所在文件夹。传承那方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
+| rec_image_shape         | 识别算法的输入图片尺寸                                                                                                                                                                                             | "3,32,320"              |
+| rec_char_type           | 识别算法的字符类型，中文(ch)或英文(en)                                                                                                                                                                               | ch                      |
+| rec_batch_num           | 进行识别时，同时前向的图片数                                                                                                                                                                                         | 30                      |
+| max_text_length         | 识别算法能识别的最大文字长度                                                                                                                                                                                         | 25                      |
+| rec_char_dict_path      | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径                                                                                                                                                | ./ppocr/utils/ppocr_keys_v1.txt                        |
+| use_space_char          | 是否识别空格                                                                                                                                                                                                         | TRUE                    |
+| enable_mkldnn           | 是否启用mkldnn                                                                                                                                                                                                       | FALSE                   |
+| det                     | 前向时使用启动检测                                                                                                                                                                                                   | TRUE                    |
+| rec                     | 前向时是否启动识别                                                                                                                                                                                                   | TRUE                    |

doc/doc_en/FAQ_en.md

@@ -45,9 +45,12 @@ At present, the open source model, dataset and magnitude are as follows:
     Among them, the public datasets are opensourced, users can search and download by themselves, or refer to [Chinese data set](./datasets_en.md), synthetic data is not opensourced, users can use open-source 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.
 
 10. **Error in using the model with TPS module for prediction**  
-Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](108) != Grid dimension[2](100)  
+Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3]\(108) != Grid dimension[2]\(100)  
 Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
 
-11. **Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**
+11. **Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**  
+The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.
 
-The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.
\ No newline at end of file
+
+12. **Results of cpp_infer and python_inference are very different**  
+Versions of exprted inference model and inference libraray should be same. For example, on Windows platform, version of the inference libraray that PaddlePaddle provides is 1.8, but version of the inference model that PaddleOCR provides is 1.7, you should export model yourself(`tools/export_model.py`) on PaddlePaddle1.8 and then use the exported model for inference.

doc/doc_en/config_en.md

@@ -60,8 +60,9 @@ Take `rec_icdar15_train.yml` as an example:
 |         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         |               \             |
 |         decay           |         Whether to use decay       |    \              |               \             |
-|      function(decay)    |         Set the decay function       |   cosine_decay    |         Support cosine_decay and piecewise_decay            |
-|      step_each_epoch    |      The number of steps in an epoch. Used in cosine_decay  |         20       | Calculation ：total_image_num / (batch_size_per_card * card_size) |
-|        total_epoch      |    The number of epochs. Used in cosine_decay      |       1000      | Consistent with Global.epoch_num      |
+|      function(decay)    |         Set the decay function       |   cosine_decay    |         Support cosine_decay, cosine_decay_warmup and piecewise_decay            |
+|      step_each_epoch    |      The number of steps in an epoch. Used in cosine_decay/cosine_decay_warmup  |         20       | Calculation: total_image_num / (batch_size_per_card * card_size) |
+|        total_epoch      |    The number of epochs. Used in cosine_decay/cosine_decay_warmup      |       1000      | Consistent with Global.epoch_num      |
+|        warmup_minibatch      |  Number of steps for linear warmup. Used in cosine_decay_warmup        |       1000      | \        |
 |        boundaries      |    The step intervals to reduce learning rate. Used in piecewise_decay       |       -      |  The format is list        |
 |        decay_rate      |    Learning rate decay rate. Used in piecewise_decay       |       -      |  \        |

doc/doc_en/detection_en.md

 # TEXT DETECTION
 
-This section uses the icdar15 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
+This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
 
 ## DATA PREPARATION
 The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.
 
 Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
-```
+```shell
 # Under the PaddleOCR path
 cd PaddleOCR/
 wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
@@ -27,16 +27,19 @@ The provided annotation file format is as follow, seperated by "\t":
 " Image file name             Image annotation information encoded by json.dumps"
 ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
 ```
-The image annotation after json.dumps() encoding is a list containing multiple dictionaries. The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
+The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries. 
+
+The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
+
+`transcription` represents the text of the current text box. **When its content is "###" it means that the text box is invalid and will be skipped during training.**
 
-`transcription` represents the text of the current text box, and this information is not needed in the text detection task.
-If you want to train PaddleOCR on other datasets, you can build the annotation file according to the above format.
+If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
 
 
 ## TRAINING
 
 First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
-```
+```shell
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
@@ -44,7 +47,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 
 # decompressing the pre-training model file, take MobileNetV3 as an example
-tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
+tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
 
 # Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows:
 ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
@@ -56,9 +59,9 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
 
 ```
 
-**START TRAINING**  
+#### START TRAINING
 *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml
 ```
 
@@ -66,19 +69,19 @@ In the above instruction, use `-c` to select the training to use the `configs/de
 For a detailed explanation of the configuration file, please refer to [config](./config_en.md).
 
 You can also use `-o` to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
 
-**load trained model and conntinue training**
+#### load trained model and conntinue training
 If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
 
 For example:
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
 ```
 
-**Note**:The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
+**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
 
 
 ## EVALUATION
@@ -89,7 +92,7 @@ Run the following code to calculate the evaluation indicators. The result will b
 
 When evaluating, set post-processing parameters `box_thresh=0.6`, `unclip_ratio=1.5`. If you use different datasets, different models for training, these two parameters should be adjusted for better result.
 
-```
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file.

doc/doc_en/inference_en.md

 
 # Reasoning based on Python prediction engine
 
-The inference model (the model saved by fluid.io.save_inference_model) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
+The inference model (the model saved by `fluid.io.save_inference_model`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
 
 The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
 
@@ -9,7 +9,31 @@ Compared with the checkpoints model, the inference model will additionally save
 
 Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model.
 
+- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
+    - [Convert detection model to inference model](#Convert_detection_model)
+    - [Convert recognition model to inference model](#Convert_recognition_model)
+    
+    
+- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
+    - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
+    - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
+    - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
+    - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
+    
+- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
+    - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
+    - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
+    - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
+    - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
+    
+    
+- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
+    - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
+    - [2. OTHER MODELS](#OTHER_MODELS)
+    
+<a name="CONVERT"></a>
 ## CONVERT TRAINING MODEL TO INFERENCE MODEL
+<a name="Convert_detection_model"></a>
 ### Convert detection model to inference model
 
 Download the lightweight Chinese detection model:
@@ -35,6 +59,7 @@ inference/det_db/
   └─  params    Check the parameter file of the inference model
 ```
 
+<a name="Convert_recognition_model"></a>
 ### Convert recognition model to inference model
 
 Download the lightweight Chinese recognition model:
@@ -62,11 +87,13 @@ After the conversion is successful, there are two files in the directory:
   └─  params    Identify the parameter files of the inference model
 ```
 
+<a name="DETECTION_MODEL_INFERENCE"></a>
 ## TEXT DETECTION MODEL INFERENCE
 
 The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model.
 Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**.
 
+<a name="LIGHTWEIGHT_DETECTION"></a>
 ### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
 
 For lightweight Chinese detection model inference, you can execute the following commands:
@@ -90,6 +117,7 @@ If you want to use the CPU for prediction, execute the command as follows
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
 ```
 
+<a name="DB_DETECTION"></a>
 ### 2. DB TEXT DETECTION MODEL INFERENCE
 
 First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)), you can use the following command to convert:
@@ -114,6 +142,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 
 **Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.
 
+<a name="EAST_DETECTION"></a>
 ### 3. EAST TEXT DETECTION MODEL INFERENCE
 
 First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)), you can use the following command to convert:
@@ -126,23 +155,64 @@ First, convert the model saved in the EAST text detection training process into
 python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
 ```
 
-For EAST text detection model inference, you need to set the parameter det_algorithm, specify the detection algorithm type to EAST, run the following command:
+**For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
 
 ```
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
 ```
+
 The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
 
 ![](../imgs_results/det_res_img_10_east.jpg)
 
-**Note**: The Python version of NMS in EAST post-processing used in this codebase so the prediction speed is quite slow. If you use the C++ version, there will be a significant speedup.
+**Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
+
+
+<a name="SAST_DETECTION"></a>
+### 4. SAST TEXT DETECTION MODEL INFERENCE
+#### (1). Quadrangle text detection model (ICDAR2015)  
+First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)), you can use the following command to convert:
+
+```
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15"
+```
+
+**For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
+
+```
+python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
+```
+
+The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
+
+![](../imgs_results/det_res_img_10_sast.jpg)
 
+#### (2). Curved text detection model (Total-Text)  
+First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
 
+```
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt"
+```
+
+**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
+
+```
+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
+```
+
+The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
+
+![](../imgs_results/det_res_img623_sast.jpg)
+
+**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
+
+<a name="RECOGNITION_MODEL_INFERENCE"></a>
 ## TEXT RECOGNITION MODEL INFERENCE
 
 The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
 
 
+<a name="LIGHTWEIGHT_RECOGNITION"></a>
 ### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
 
 For lightweight Chinese recognition model inference, you can execute the following commands:
@@ -158,6 +228,7 @@ After executing the command, the prediction results (recognized text and score)
 Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
 
 
+<a name="CTC-BASED_RECOGNITION"></a>
 ### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE
 
 Taking STAR-Net as an example, we introduce the recognition model inference based on CTC loss. CRNN and Rosetta are used in a similar way, by setting the recognition algorithm parameter `rec_algorithm`.
@@ -178,6 +249,7 @@ For STAR-Net text recognition model inference, execute the following commands:
 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_type="en"
 ```
 
+<a name="ATTENTION-BASED_RECOGNITION"></a>
 ### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
 ![](../imgs_words_en/word_336.png)
 
@@ -196,6 +268,7 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
 
+<a name="USING_CUSTOM_CHARACTERS"></a>
 ### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
 If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
 
@@ -203,8 +276,10 @@ If the chars dictionary is modified during training, you need to specify the new
 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_type="en" --rec_char_dict_path="your text dict path"
 ```
 
+<a name="CONCATENATION"></a>
 ## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
 
+<a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
 ### 1. LIGHTWEIGHT CHINESE MODEL
 
 When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
@@ -217,9 +292,14 @@ After executing the command, the recognition result image is as follows:
 
 ![](../imgs_results/2.jpg)
 
+<a name="OTHER_MODELS"></a>
 ### 2. OTHER MODELS
 
-If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model, the following command uses the combination of the EAST text detection and STAR-Net text recognition:
+If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model.
+
+**Note: due to the limitation of rotation logic of detected box, SAST curved text detection model (using the parameter `det_sast_polygon=True`) is not supported for model combination yet.**
+
+The following command uses the combination of the EAST text detection and STAR-Net text recognition:
 
 ```
 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_type="en"

doc/doc_en/quickstart_en.md

@@ -5,6 +5,7 @@
 
 Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
 
+*Note: Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)。*
 
 ## 2.inference models
 

doc/doc_en/recognition_en.md

@@ -18,6 +18,8 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 
 If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark
 
+If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
+
 * Use your own dataset:
 
 If you want to use your own data for training, please refer to the following to organize your data.

doc/doc_en/update_en.md

 # RECENT UPDATES
+- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
 - 2020.8.16 Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
 - 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
 - 2020.7.15, Add mobile App demo , support both iOS and  Android  ( based on easyedge and Paddle Lite)