Merge pull request #1 from PaddlePaddle/develop

update-2020-7-17

Merge pull request #1 from PaddlePaddle/develop
update-2020-7-17
26219d5f · shaohua.zhang · GitHub · 0e8a3417 · 311c5997 · 26219d5f
Unverified Commit 26219d5f authored Jul 17, 2020 by shaohua.zhang Committed by GitHub Jul 17, 2020
20 changed files
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -23,7 +23,7 @@ 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：
+提供的标注文件格式为，其中中间是"\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]], ...}]
@@ -35,7 +35,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中

 ## 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，
+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。
 ```
 cd PaddleOCR/
@@ -43,37 +43,63 @@ cd PaddleOCR/
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
 # 下载ResNet50的预训练模型
 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/
+
+# 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/
+  └─ conv_last_bn_mean
+  └─ conv_last_bn_offset
+  └─ conv_last_bn_scale
+  └─ conv_last_bn_variance
+  └─ ......
+
 ```

 **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*
+
 ```
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
 ```

 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](./doc/config.md)。
+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
 ```
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```

+**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：
+```
+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。
+
 ## 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。

+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
 ```
-python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy"
+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。

 比如：
 ```
-python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./output/det_db/best_accuracy"
+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

 ## 测试检测效果

@@ -82,6 +108,12 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./ou
 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，
+```
+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
 ```
 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"

--- a/doc/doc_ch/handwritten_datasets.md
+++ b/doc/doc_ch/handwritten_datasets.md
+# 手写OCR数据集
+这里整理了常用手写数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+- [中科院自动化研究所-手写中文数据集](#中科院自动化研究所-手写中文数据集)
+- [NIST手写单字数据集-英文](#NIST手写单字数据集-英文)
+
+<a name="中科院自动化研究所-手写中文数据集"></a>
+## 中科院自动化研究所-手写中文数据集
+- **数据来源**：http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
+- **数据简介**：
+    * 包含在线和离线两类手写数据，`HWDB1.0~1.2`总共有3895135个手写单字样本，分属7356类（7185个汉字和171个英文字母、数字、符号）；`HWDB2.0~2.2`总共有5091页图像，分割为52230个文本行和1349414个文字。所有文字和文本样本均存为灰度图像。部分单字样本图片如下所示。
+
+        ![](../datasets/CASIA_0.jpg)
+
+- **下载地址**：http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
+- **使用建议**：数据为单字，白色背景，可以大量合成文字行进行训练。白色背景可以处理成透明状态，方便添加各种背景。对于需要语义的情况，建议从真实语料出发，抽取单字组成文字行
+
+
+<a name="NIST手写单字数据集-英文"></a>
+## NIST手写单字数据集-英文(NIST Handprinted Forms and Characters Database)
+
+- **数据来源**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
+
+- **数据简介**: NIST19数据集适用于手写文档和字符识别的模型训练，从3600位作者的手写样本表格中提取得到，总共包含81万张字符图片。其中9张图片示例如下。
+
+    ![](../datasets/nist_demo.png)
+
+
+- **下载地址**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
--- a/doc/inference.md
+++ b/doc/inference.md

-# 基于预测引擎推理
+# 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。
@@ -8,22 +8,27 @@ inference 模型（fluid.io.save_inference_model保存的模型）

 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。

-## 训练模型转inference模型
+## 一、训练模型转inference模型
 ### 检测模型转inference模型

 下载超轻量级中文检测模型：
 ```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/det_mv3_db.tar && tar xf ch_lite/det_mv3_db.tar -C ch_lite
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
 ```
 上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成inference模型只需要运行如下命令：
 ```
-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_model/det_db/
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/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：
 ```
-inference_model/det_db/
+inference/det_db/
  └─  model     检测inference模型的program文件
  └─  params    检测inference模型的参数文件
 ```
@@ -32,24 +37,30 @@ inference_model/det_db/

 下载超轻量中文识别模型：
 ```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/rec_crnn.tar && tar xf ch_lite/rec_crnn.tar -C .ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
 ```

 识别模型转inference模型与检测的方式相同，如下：
 ```
-python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_crnn/best_accuracy \
-        Global.save_inference_dir=./inference_model/rec_crnn/
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
+        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。

 转换成功后，在目录下有两个文件：
 ```
-/inference_model/rec_crnn/
+/inference/rec_crnn/
  └─  model     识别inference模型的program文件
  └─  params    识别inference模型的参数文件
 ```

-## 文本检测模型推理
+## 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。

@@ -58,7 +69,7 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
 超轻量中文检测模型推理，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/"
+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：
@@ -68,12 +79,12 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 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/" --det_max_side_len=1200
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200
 ```

 如果想使用CPU进行预测，执行命令如下
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det/" --use_gpu=False
+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
@@ -96,7 +107,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_

 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)
+![](../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。

@@ -115,18 +126,18 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.check
 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：

 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
+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：

-![](imgs_results/det_res_img_10_east.jpg)
+![](../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。


-## 文本识别模型推理
+## 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和基于CTC损失的识别模型推理。**而基于Attention损失的识别模型推理还在调试中**。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。
+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
@@ -134,10 +145,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 超轻量中文识别模型推理，可以执行如下命令：

 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec/"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
 ```

-![](imgs_words/ch/word_4.jpg)
+![](../imgs_words/ch/word_4.jpg)

 执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：

@@ -164,7 +175,17 @@ 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"
 ```
-![](imgs_words_en/word_336.png)
+
+### 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"
+
+RARE 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/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
+```
+
+![](../imgs_words_en/word_336.png)

 执行命令后，上面图像的识别结果如下：

@@ -181,19 +202,26 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```

-## 文本检测、识别串联推理
+### 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"
+```
+
+## 四、文本检测、识别串联推理

 ### 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。

 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
 ```

 执行命令后，识别结果图像如下：

-![](imgs_results/2.jpg)
+![](../imgs_results/2.jpg)

 ### 2.其他模型推理

@@ -205,4 +233,4 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d

 执行命令后，识别结果图像如下：

-![](imgs_results/img_10.jpg)
+![](../imgs_results/img_10.jpg)
--- a/doc/installation.md
+++ b/doc/installation.md
 ## 快速安装

+经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
+PaddleOCR 工作环境
+- PaddlePaddle 1.7+
+- python3
+- glibc 2.23
+- cuDNN 7.6+ (GPU)
+
 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。
+
+*如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。*
+
+1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。
 ```
 # 切换到工作目录下
 cd /home/Projects
 # 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
 # 创建一个名字为ppocr的docker容器，并将当前目录映射到容器的/paddle目录下

+如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
+
 如果您的机器安装的是CUDA9，请运行以下命令创建容器
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash

@@ -17,10 +30,24 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidu
 您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。

 # ctrl+P+Q可退出docker，重新进入docker使用如下命令
-sudo nvidia-docker container exec -it ppocr /bin/bash
+sudo docker container exec -it ppocr /bin/bash
+```
+
+注意：如果docker pull过慢，可以按照如下步骤手动下载后加载docker,以cuda9 docker为例，使用cuda10 docker只需要将cuda9改为cuda10即可。
+```
+# 下载CUDA9 docker的压缩文件，并解压
+wget https://paddleocr.bj.bcebos.com/docker/docker_pdocr_cuda9.tar.gz
+# 为减少下载时间，上传的docker image是压缩过的，需要解压使用
+tar zxf docker_pdocr_cuda9.tar.gz
+# 创建image
+docker load < docker_pdocr_cuda9.tar
+# 完成上述步骤后通过docker images检查是否加载了下载的镜像
+docker images
+# 执行docker images后如果有下面的输出，即可按照按照 步骤1 创建docker环境。
+hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
 ```

-2. 安装PaddlePaddle Fluid v1.7(暂不支持更高版本,适配工作进行中)
+2. 安装PaddlePaddle Fluid v1.7
 ```
 pip3 install --upgrade pip

@@ -30,12 +57,22 @@ python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsing
 如果您的机器安装的是CUDA10，请运行以下命令安装
 python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple

+如果您的机器是CPU，请运行以下命令安装
+
+python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
+
 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
 ```

 3. 克隆PaddleOCR repo代码
 ```
-git clone https://github.com/PaddlePaddle/PaddleOCR
+【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
+
+如果因为网络问题无法pull成功，也可选择使用码云上的托管：
+
+git clone https://gitee.com/paddlepaddle/PaddleOCR
+
+注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
 ```

 4. 安装第三方库

--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
+
+# 中文OCR模型快速使用
+
+## 1.环境配置
+
+请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+
+## 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|
+|-|-|-|-|-|
+|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
+|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
+
+*windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下*
+
+复制上表中的检测和识别的`inference模型`下载地址，并解压
+
+```
+mkdir inference && cd inference
+# 下载检测模型并解压
+wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
+# 下载识别模型并解压
+wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
+cd ..
+```
+
+以超轻量级模型为例：
+
+```
+mkdir inference && cd inference
+# 下载超轻量级中文OCR模型的检测模型并解压
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+# 下载超轻量级中文OCR模型的识别模型并解压
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+cd ..
+```
+
+解压完毕后应有如下文件结构：
+
+```
+|-inference
+    |-ch_rec_mv3_crnn
+        |- model
+        |- params
+    |-ch_det_mv3_db
+        |- model
+        |- params
+    ...
+```
+
+## 3.单张图像或者图像集合预测
+
+以下代码实现了文本检测、识别串联推理，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。
+
+```bash
+
+# 预测image_dir指定的单张图像
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+
+# 预测image_dir指定的图像集合
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+
+# 如果想使用CPU进行预测，需设置use_gpu参数为False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
+```
+
+- 通用中文OCR模型
+
+请按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
+```
+# 预测image_dir指定的单张图像
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
+```
+
+- 支持空格的通用中文OCR模型
+
+请按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
+
+*注意：请将代码更新到最新版本，并添加参数 `--use_space_char=True` *
+
+```
+# 预测image_dir指定的单张图像
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/" --use_space_char=True
+```
+
+更多的文本检测、识别串联推理使用方式请参考文档教程中[基于Python预测引擎推理](./inference.md)。
+
+此外，文档教程中也提供了中文OCR模型的其他预测部署方式：
+- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
+- [服务部署](./serving.md)
+- [端侧部署](../../deploy/lite/readme.md)
--- a/doc/recognition.md
+++ b/doc/recognition.md
@@ -10,7 +10,7 @@ PaddleOCR 支持两种数据格式: `lmdb` 用于训练公开数据，调试算
 训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：

 ```
-ln -sf <path/to/dataset> <path/to/paddle_detection>/train_data/dataset
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 ```


@@ -41,6 +41,8 @@ PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通
 wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
 # 测试集标签
 wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
+
+
 ```

 最终训练集应有如下文件结构：
@@ -92,7 +94,17 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 `ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典，
 您可以按需使用。

-如需自定义dic文件，请修改 `configs/rec/rec_icdar15_train.yml` 中的 `character_dict_path` 字段, 并将 `character_type` 设置为 `ch`。
+- 自定义字典
+
+如需自定义dic文件，请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
+并将 `character_type` 设置为 `ch`。
+
+- 添加空格类别
+
+如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `true`。
+
+**注意：`use_space_char` 仅在 `character_type=ch` 时生效**
+

 ### 启动训练

@@ -111,6 +123,8 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar

 开始训练:

+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+
 ```
 # 设置PYTHONPATH路径
 export PYTHONPATH=$PYTHONPATH:.
@@ -120,6 +134,18 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
 ```

+- 数据增强
+
+PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
+
+默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)。
+
+训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+
+*由于OpenCV的兼容性问题，扰动操作暂时只支持GPU*
+
+- 训练
+
 PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/rec_CRNN/best_accuracy` 。

 如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
@@ -153,12 +179,26 @@ Global:
  character_type: ch
  # 添加自定义字典，如修改字典请将路径指向新字典
  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
+  # 训练时添加数据增强
+  distort: true
+  # 识别空格
+  use_space_char: true
  ...
  # 修改reader类型
  reader_yml: ./configs/rec/rec_chinese_reader.yml
  ...

 ...
+
+Optimizer:
+  ...
+  # 添加学习率衰减策略
+  decay:
+    function: cosine_decay
+    # 每个 epoch 包含 iter 数
+    step_each_epoch: 20
+    # 总共训练epoch数
+    total_epoch: 1000
 ```
 **注意，预测/评估时的配置文件请务必与训练一致。**

@@ -168,10 +208,11 @@ Global:

 评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml`  修改EvalReader中的 `label_file_path` 设置。

+*注意* 评估时必须确保配置文件中 infer_img 字段为空
 ```
 export CUDA_VISIBLE_DEVICES=0
 # GPU 评估， Global.checkpoints 为待测权重
-python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
 ```

 ### 预测
@@ -184,12 +225,12 @@ python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkp

 ```
 # 预测英文结果
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
 ```

 预测图片：

-![](./imgs_words/en/word_1.png)
+![](../imgs_words/en/word_1.png)

 得到输入图像的预测结果：

@@ -204,12 +245,12 @@ infer_img: doc/imgs_words/en/word_1.png

 ```
 # 预测中文结果
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```

 预测图片：

-![](./imgs_words/ch/word_1.jpg)
+![](../imgs_words/ch/word_1.jpg)

 得到输入图像的预测结果：


--- a/doc/doc_ch/reference.md
+++ b/doc/doc_ch/reference.md
+# 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}
+}
+```
\ No newline at end of file
--- a/doc/doc_ch/serving.md
+++ b/doc/doc_ch/serving.md
+# 服务部署
+
+PaddleOCR提供2种服务部署方式：
+- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)），按照本教程使用；
+- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。  
+
+服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
+```
+deploy/hubserving/
+  └─  ocr_det     检测模块服务包
+  └─  ocr_rec     识别模块服务包
+  └─  ocr_system  检测+识别串联服务包
+```
+
+每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下：
+```
+deploy/hubserving/ocr_system/
+  └─  __init__.py    空文件，必选
+  └─  config.json    配置文件，可选，使用配置启动服务时作为参数传入
+  └─  module.py      主模块，必选，包含服务的完整逻辑
+  └─  params.py      参数文件，必选，包含模型路径、前后处理参数等参数
+```
+
+## 快速启动服务
+以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
+### 1. 准备环境
+```shell
+# 安装paddlehub  
+pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# 设置环境变量  
+export PYTHONPATH=.
+```   
+
+### 2. 安装服务模块
+PaddleOCR提供3种服务模块，根据需要安装所需模块。如： 
+
+安装检测服务模块：  
+```hub install deploy/hubserving/ocr_det/```  
+
+或，安装识别服务模块：    
+```hub install deploy/hubserving/ocr_rec/```  
+
+或，安装检测+识别串联服务模块：  
+```hub install deploy/hubserving/ocr_system/```  
+
+### 3. 启动服务
+#### 方式1. 命令行命令启动（仅支持CPU）
+**启动命令：**  
+```shell
+$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
+                    --port XXXX \
+                    --use_multiprocess \
+                    --workers \
+```  
+
+**参数：**  
+
+|参数|用途|  
+|-|-|  
+|--modules/-m|PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出<br>*`当不指定Version时，默认选择最新版本`*|  
+|--port/-p|服务端口，默认为8866|  
+|--use_multiprocess|是否启用并发方式，默认为单进程方式，推荐多核CPU机器使用此方式<br>*`Windows操作系统只支持单进程方式`*|
+|--workers|在并发方式下指定的并发任务数，默认为`2*cpu_count-1`，其中`cpu_count`为CPU核数|  
+
+如启动串联服务：  ```hub serving start -m ocr_system```  
+
+这样就完成了一个服务化API的部署，使用默认端口号8866。
+
+#### 方式2. 配置文件启动（支持CPU、GPU）
+**启动命令：**  
+```hub serving start --config/-c config.json```  
+
+其中，`config.json`格式如下： 
+```python
+{
+    "modules_info": {
+        "ocr_system": {
+            "init_args": {
+                "version": "1.0.0",
+                "use_gpu": true
+            },
+            "predict_args": {
+            }
+        }
+    },
+    "port": 8868,
+    "use_multiprocess": false,
+    "workers": 2
+}
+```
+
+- `init_args`中的可配参数与`module.py`中的`_initialize`函数接口一致。其中，**当`use_gpu`为`true`时，表示使用GPU启动服务**。  
+- `predict_args`中的可配参数与`module.py`中的`predict`函数接口一致。
+
+**注意:**  
+- 使用配置文件启动服务时，其他参数会被忽略。
+- 如果使用GPU预测(即，`use_gpu`置为`true`)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。
+- **`use_gpu`不可与`use_multiprocess`同时为`true`**。
+
+如，使用GPU 3号卡启动串联服务：  
+```shell
+export CUDA_VISIBLE_DEVICES=3
+hub serving start -c deploy/hubserving/ocr_system/config.json
+```  
+
+## 发送预测请求
+配置好服务端，可使用以下命令发送预测请求，获取预测结果:  
+
+```python tools/test_hubserving.py server_url image_path```  
+
+需要给脚本传递2个参数：  
+- **server_url**：服务地址，格式为  
+`http://[ip_address]:[port]/predict/[module_name]`  
+例如，如果使用配置文件启动检测、识别、检测+识别2阶段服务，那么发送请求的url将分别是：  
+`http://127.0.0.1:8866/predict/ocr_det`  
+`http://127.0.0.1:8867/predict/ocr_rec`  
+`http://127.0.0.1:8868/predict/ocr_system`  
+- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径  
+
+访问示例：  
+```python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/```
+
+## 返回结果格式说明
+返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下：
+
+|字段名称|数据类型|意义| 
+|-|-|-|
+|text|str|文本内容|
+|confidence|float| 文本识别置信度|
+|text_region|list|文本位置坐标|
+
+不同模块返回的字段不同，如，文本识别服务模块返回结果不含`text_region`字段，具体信息如下：
+
+|字段名/模块名|ocr_det|ocr_rec|ocr_system|
+|-|-|-|-|  
+|text||✔|✔| 
+|confidence||✔|✔| 
+|text_region|✔||✔| 
+
+**说明：** 如果需要增加、删除、修改返回字段，可在相应模块的`module.py`文件中进行修改，完整流程参考下一节自定义修改服务模块。
+
+## 自定义修改服务模块
+如果需要修改服务逻辑，你一般需要操作以下步骤（以修改`ocr_system`为例）：  
+
+- 1、 停止服务  
+```hub serving stop --port/-p XXXX```  
+
+- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。  
+例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。
+
+- 3、 卸载旧服务包  
+```hub uninstall ocr_system```  
+
+- 4、 安装修改后的新服务包  
+```hub install deploy/hubserving/ocr_system/```  
+
+- 5、重新启动服务  
+```hub serving start -m ocr_system```  
+
--- a/doc/doc_ch/tricks.md
+++ b/doc/doc_ch/tricks.md
+## 中文OCR训练预测技巧
+这里整理了一些中文OCR训练预测技巧，持续更新中，欢迎各位小伙伴贡献OCR炼丹秘籍～
+- [更换骨干网络](#更换骨干网络)
+- [中文长文本识别](#中文长文本识别)
+- [空格识别](#空格识别)
+
+<a name="更换骨干网络"></a>
+#### 1、更换骨干网络
+- **问题描述**
+
+  目前PaddleOCR中使用的骨干网络有ResNet_vd系列和MobileNetV3系列，更换骨干网络是否有助于效果提升？更换时需要注意什么？
+
+- **炼丹建议**
+
+  - 无论是文字检测，还是文字识别，骨干网络的选择是预测效果和预测效率的权衡。一般，选择更大规模的骨干网络，例如ResNet101_vd，则检测或识别更准确，但预测耗时相应也会增加。而选择更小规模的骨干网络，例如MobileNetV3_small_x0_35，则预测更快，但检测或识别的准确率会大打折扣。幸运的是不同骨干网络的检测或识别效果与在ImageNet数据集图像1000分类任务效果正相关。[**飞桨图像分类套件PaddleClas**](https://github.com/PaddlePaddle/PaddleClas)汇总了ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet等23种系列的分类网络结构，在上述图像分类任务的top1识别准确率，GPU(V100和T4)和CPU(骁龙855)的预测耗时以及相应的[**117个预训练模型下载地址**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。
+  - 文字检测骨干网络的替换，主要是确定类似与ResNet的4个stages，以方便集成后续的类似FPN的检测头。此外，对于文字检测问题，使用ImageNet训练的分类预训练模型，可以加速收敛和效果提升。
+  - 文字识别的骨干网络的替换，需要注意网络宽高stride的下降位置。由于文本识别一般宽高比例很大，因此高度下降频率少一些，宽度下降频率多一些。可以参考PaddleOCR中[MobileNetV3骨干网络](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py)的改动。
+
+<a name="中文长文本识别"></a>
+#### 2、中文长文本识别
+- **问题描述**
+  
+  中文识别模型训练时分辨率最大是[3,32,320]，如果待识别的文本图像太长，如下图所示，该如何适配？
+  
+  <div align="center">
+    <img src="../tricks/long_text_examples.jpg" width="600">
+  </div>
+  
+- **炼丹建议**
+    
+  在中文识别模型训练时，并不是采用直接将训练样本缩放到[3,32,320]进行训练，而是先等比例缩放图像，保证图像高度为32，宽度不足320的部分补0，宽高比大于10的样本直接丢弃。预测时，如果是单张图像预测，则按上述操作直接对图像缩放，不做宽度320的限制。如果是多张图预测，则采用batch方式预测，每个batch的宽度动态变换，采用这个batch中最长宽度。[参考代码如下](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/tools/infer/predict_rec.py)：
+  
+  ```
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+        assert imgC == img.shape[2]
+        if self.character_type == "ch":
+            imgW = int((32 * max_wh_ratio))
+        h, w = img.shape[:2]
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im  
+  ```
+
+<a name="空格识别"></a>
+#### 3、空格识别
+- **问题描述**
+
+   如下图所示，对于中英文混合场景，为了便于阅读和使用识别结果，往往需要将单词之间的空格识别出来，这种情况如何适配？
+   
+  <div align="center">
+    <img src="../imgs_results/chinese_db_crnn_server/en_paper.jpg" width="600">
+  </div>
+  
+- **炼丹建议**
+  
+   空格识别可以考虑以下两种方案:(1)优化文本检测算法。检测结果在空格处将文本断开。这种方案在检测数据标注时，需要将含有空格的文本行分成好多段。(2)优化文本识别算法。在识别字典里面引入空格字符，然后在识别的训练数据中，如果用空行，进行标注。此外，合成数据时，通过拼接训练数据，生成含有空格的文本。PaddleOCR目前采用的是第二种方案。
+   
\ No newline at end of file
--- a/doc/doc_ch/update.md
+++ b/doc/doc_ch/update.md
+# 更新
+- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
+- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
+- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
+- 2020.7.9 添加支持空格的识别模型，识别效果，预测及训练方式请参考快速开始和文本识别训练相关文档
+- 2020.7.9 添加数据增强、学习率衰减策略,具体参考[配置文件](./doc/doc_ch/config.md)
+- 2020.6.8 添加[数据集](./doc/doc_ch/datasets.md)，并保持持续更新
+- 2020.6.5 支持 `attetnion` 模型导出 `inference_model`
+- 2020.6.5 支持单独预测识别时，输出结果得分
+- 2020.5.30 提供超轻量级中文OCR在线体验
+- 2020.5.30 模型预测、训练支持Windows系统
+- 2020.5.30 开源通用中文OCR模型
+- 2020.5.14 发布[PaddleOCR公开课](https://www.bilibili.com/video/BV1nf4y1U7RX?p=4)
+- 2020.5.14 发布[PaddleOCR实战练习](https://aistudio.baidu.com/aistudio/projectdetail/467229)
+- 2020.5.14 开源8.6M超轻量级中文OCR模型
--- a/doc/doc_ch/vertical_and_multilingual_datasets.md
+++ b/doc/doc_ch/vertical_and_multilingual_datasets.md
+# 垂类多语言OCR数据集
+这里整理了常用垂类和多语言OCR数据集，持续更新中，欢迎各位小伙伴贡献数据集～
+- [中国城市车牌数据集](#中国城市车牌数据集)
+- [银行信用卡数据集](#银行信用卡数据集)
+- [验证码数据集-Captcha](#验证码数据集-Captcha)
+- [多语言数据集](#多语言数据集)
+
+
+<a name="中国城市车牌数据集"></a>
+## 中国城市车牌数据集
+
+- **数据来源**：[https://github.com/detectRecog/CCPD](https://github.com/detectRecog/CCPD)
+
+- **数据简介**: 包含超过25万张中国城市车牌图片及车牌检测、识别信息的标注。包含以下几种不同场景中的车牌图片信息。
+    * CCPD-Base: 通用车牌图片
+    * CCPD-DB: 车牌区域亮度较亮、较暗或者不均匀
+    * CCPD-FN: 车牌离摄像头拍摄位置相对更远或者更近
+    * CCPD-Rotate: 车牌包含旋转（水平20\~50度，竖直-10\~10度）
+    * CCPD-Tilt: 车牌包含旋转（水平15\~45度，竖直15\~45度）
+    * CCPD-Blur: 车牌包含由于摄像机镜头抖动导致的模糊情况
+    * CCPD-Weather: 车牌在雨天、雪天或者雾天拍摄得到
+    * CCPD-Challenge: 至今在车牌检测识别任务中最有挑战性的一些图片
+    * CCPD-NP: 没有安装车牌的新车图片。
+
+    ![](../datasets/ccpd_demo.png)
+
+
+- **下载地址**
+    * 百度云下载地址(提取码是hm0U): [https://pan.baidu.com/s/1i5AOjAbtkwb17Zy-NQGqkw](https://pan.baidu.com/s/1i5AOjAbtkwb17Zy-NQGqkw)
+    * Google drive下载地址：[https://drive.google.com/file/d/1rdEsCUcIUaYOVRkx5IMTRNA7PcGMmSgc/view](https://drive.google.com/file/d/1rdEsCUcIUaYOVRkx5IMTRNA7PcGMmSgc/view)
+
+
+<a name="银行信用卡数据集"></a>
+## 银行信用卡数据集
+
+- **数据来源**: [https://www.kesci.com/home/dataset/5954cf1372ead054a5e25870](https://www.kesci.com/home/dataset/5954cf1372ead054a5e25870)
+
+- **数据简介**: 训练数据共提供了三类数据
+    * 1.招行样卡数据： 包括卡面图片数据及标注数据，总共618张图片
+    * 2.单字符数据： 包括图片及标注数据，总共37张图片。
+    * 3.仅包含其他银行卡面，不具有更细致的信息，总共50张图片。
+
+    * demo图片展示如下，标注信息存储在excel表格中，下面的demo图片标注为
+        * 前8位卡号：62257583
+        * 卡片种类：本行卡
+        * 有效期结束：07/41
+        * 卡用户拼音：MICHAEL
+
+    ![](../datasets/cmb_demo.jpg)
+
+- **下载地址**: [https://cdn.kesci.com/cmb2017-2.zip](https://cdn.kesci.com/cmb2017-2.zip)
+
+
+
+<a name="验证码数据集-Captcha"></a>
+## 验证码数据集-Captcha
+
+- **数据来源**: [https://github.com/lepture/captcha](https://github.com/lepture/captcha)
+
+- **数据简介**: 这是一个数据合成的工具包，可以根据输入的文本，输出验证码图片，使用该工具包生成几张demo图片如下。
+
+    ![](../datasets/captcha_demo.png)
+
+- **下载地址**: 该数据集是生成得到，无下载地址。
+
+
+
+<a name="多语言数据集"></a>
+## 多语言数据集(Multi-lingual scene text detection and recognition)
+
+- **数据来源**: [https://rrc.cvc.uab.es/?ch=15&com=downloads](https://rrc.cvc.uab.es/?ch=15&com=downloads)
+
+- **数据简介**: 多语言检测数据集MLT同时包含了语种识别和检测任务。
+    * 在检测任务中，训练集包含10000张图片，共有10种语言，每种语言包含1000张训练图片。测试集包含10000张图片。
+    * 在识别任务中，训练集包含111998个样本。
+
+
+- **下载地址**: 训练集较大，分2部分下载，需要在网站上注册之后才能下载：
+[https://rrc.cvc.uab.es/?ch=15&com=downloads](https://rrc.cvc.uab.es/?ch=15&com=downloads)
--- a/doc/doc_ch/visualization.md
+++ b/doc/doc_ch/visualization.md
+# 效果展示
+- [超轻量级中文OCR效果展示](#超轻量级中文OCR)
+- [通用中文OCR效果展示](#通用中文OCR)
+- [支持空格的中文OCR效果展示](#支持空格的中文OCR)
+
+<a name="超轻量级中文OCR"></a>
+## 超轻量级中文OCR效果展示
+
+<div align="center">
+    <img src="../imgs_results/1.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/7.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/12.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/4.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/6.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/9.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/16.png" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/22.jpg" width="800">
+</div>
+
+<a name="通用中文OCR"></a>
+## 通用中文OCR效果展示
+
+<div align="center">
+    <img src="../imgs_results/chinese_db_crnn_server/11.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/chinese_db_crnn_server/2.jpg" width="800">
+</div>
+
+<div align="center">
+    <img src="../imgs_results/chinese_db_crnn_server/8.jpg" width="800">
+</div>
+
+<a name="支持空格的中文OCR"></a>
+## 支持空格的中文OCR效果展示
+
+### 轻量级模型
+<div align="center">
+    <img src="../imgs_results/img_11.jpg" width="800">
+</div>
+
+### 通用模型
+<div align="center">
+    <img src="../imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
+</div>
--- a/doc/doc_en/FAQ_en.md
+++ b/doc/doc_en/FAQ_en.md
+## FAQ
+
+1. **Prediction error: got an unexpected keyword argument 'gradient_clip'**  
+The installed version of paddle is incorrect. Currently, this project only supports paddle1.7, which will be adapted to 1.8 in the near future.
+
+2. **Error when converting attention recognition model: KeyError: 'predict'**  
+Solved. Please update to the latest version of the code.
+
+3. **About inference speed**  
+When there are many words in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch num. The default value is 30, which can be changed to 10 or other values.
+
+4. **Service deployment and mobile deployment**  
+It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
+
+5. **Release time of self-developed algorithm**  
+Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
+
+6. **How to run on Windows or Mac?**  
+PaddleOCR has completed the adaptation to Windows and MAC systems. Two points should be noted during operation:
+    1. In [Quick installation](./installation_en.md), if you do not want to install docker, you can skip the first step and start with the second step.
+    2. When downloading the inference model, if wget is not installed, you can directly click the model link or copy the link address to the browser to download, then extract and place it in the corresponding directory.
+
+7. **The difference between ultra-lightweight model and General OCR model**  
+At present, PaddleOCR has opensourced two Chinese models, namely 8.6M ultra-lightweight Chinese model and general Chinese OCR model. The comparison information between the two is as follows:
+    - Similarities: Both use the same **algorithm** and **training data**；  
+    - Differences: The difference lies in **backbone network** and **channel parameters**, the ultra-lightweight model uses MobileNetV3 as the backbone network, the general model uses Resnet50_vd as the detection model backbone, and Resnet34_vd as the recognition model backbone. You can compare the two model training configuration files to see the differences in parameters.
+
+|Model|Backbone|Detection configuration file|Recognition configuration file|
+|-|-|-|-|
+|8.6M ultra-lightweight Chinese OCR model|MobileNetV3+MobileNetV3|det_mv3_db.yml|rec_chinese_lite_train.yml|
+|General Chinese OCR model|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml|
+
+8. **Is there a plan to opensource a model that only recognizes numbers or only English + numbers?**  
+It is not planned to opensource numbers only, numbers + English only, or other vertical text models. Paddleocr has opensourced a variety of detection and recognition algorithms for customized training. The two Chinese models are also based on the training output of the open-source algorithm library. You can prepare the data according to the tutorial, choose the appropriate configuration file, train yourselves, and we believe that you can get good result. If you have any questions during the training, you are welcome to open issues or ask in the communication group. We will answer them in time.
+
+9. **What is the training data used by the open-source model? Can it be opensourced?**  
+At present, the open source model, dataset and magnitude are as follows:
+    - Detection:  
+    English dataset: ICDAR2015  
+    Chinese dataset: LSVT street view dataset with 3w pictures
+    - Recognition:  
+    English dataset: MJSynth and SynthText synthetic dataset, the amount of data is tens of millions.  
+    Chinese dataset: LSVT street view dataset with cropped text area, a total of 30w images. In addition, the synthesized data based on LSVT corpus is 500w.
+
+    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)  
+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**
+
+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
--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
+# OPTIONAL PARAMETERS LIST
+
+The following list can be viewed via `--help`
+
+|         FLAG             |     Supported script    |        Use        |      Defaults       |         Note         |
+| :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: |
+|          -c              |      ALL       |  Specify configuration file to use |  None  |  **Please refer to the parameter introduction for configuration file usage** |
+|          -o              |      ALL       |  set configuration options  |  None  |  Configuration using -o has higher priority than the configuration file selected with -c. E.g: `-o Global.use_gpu=false`  |  
+
+
+## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
+
+Take `rec_chinese_lite_train.yml` as an example
+
+
+|         Parameter             |            Use                |      Default       |            Note            |
+| :----------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
+|      algorithm           |    Select algorithm to use                    |  Synchronize with configuration file   |     For selecting model, please refer to the supported model [list](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/README_en.md) |
+|      use_gpu             |    Set using GPU or not            |       true        |                \                 |
+|      epoch_num           |    Maximum training epoch number             |       3000        |                \                 |
+|      log_smooth_window   |    Sliding window size            |       20          |                \                 |
+|      print_batch_step    |    Set print log interval         |       10          |                \                 |
+|      save_model_dir      |    Set model save path        |  output/{model_name}  |                \                 |
+|      save_epoch_step     |    Set model save interval        |       3           |                \                 |
+|      eval_batch_step     |    Set the model evaluation interval        |2000 or [1000, 2000] |runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration  |
+|train_batch_size_per_card |  Set the batch size during training   |         256         |                \                 |
+| test_batch_size_per_card |  Set the batch size during testing    |         256         |                \                 |
+|      image_shape         |    Set input image size        |   [3, 32, 100]    |                \                 |
+|      max_text_length     |    Set the maximum text length        |       25          |                \                 |
+|      character_type      |    Set character type            |       ch          |    en/ch, the default dict will be used for en, and the custom dict will be used for ch|
+|      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ic15_dict.txt  |    \                 |
+|      loss_type           |    Set loss type              |       ctc         |    Supports two types of loss: ctc / attention |
+|       distort            |    Set use distort          |       false       |  Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)                 |
+|      use_space_char          |    Wether to recognize space             |        false      |         Only support in character_type=ch mode                 |
+|      reader_yml          |    Set the reader configuration file          |  ./configs/rec/rec_icdar15_reader.yml  |  \          |
+|      pretrain_weights    |    Load pre-trained model path      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
+|      checkpoints         |    Load saved model path            |       None        |    Used to load saved parameters to continue training after interruption |
+|      save_inference_dir  |   path to save model for inference |          None        |   Use to save inference model |
+
+## INTRODUCTION TO READER PARAMETERS OF CONFIGURATION FILE
+
+Take `rec_chinese_reader.yml` as an example:
+
+|         Parameter             |            Use                |      Default       |            Note            |
+| :----------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
+|      reader_function     |    Select data reading method        |  ppocr.data.rec.dataset_traversal,SimpleReader  | Support two data reading methods: SimpleReader / LMDBReader  |
+|      num_workers             |    Set the number of data reading threads            |       8        |                \                 |
+|      img_set_dir          |    Image folder path             |       ./train_data        |                \                 |
+|      label_file_path      |    Groundtruth file path           |       ./train_data/rec_gt_train.txt| \    |
+|      infer_img            |    Result folder path     |       ./infer_img | \|
+
+## INTRODUCTION TO OPTIMIZER PARAMETERS OF CONFIGURATION FILE
+
+Take `rec_icdar15_train.yml` as an example:
+
+|         Parameter             |            Use          |      Default        |            None             |
+| :---------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
+|         function        |         Select Optimizer function          |  pocr.optimizer,AdamDecay  |  Only support Adam  |
+|         base_lr         |      Set the base lr          |       0.0005      |               \             |
+|         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    |           Only support cosine_decay            |
+|      step_each_epoch    |      The number of steps in an epoch.  |         20       | Calculation ：total_image_num / (batch_size_per_card * card_size) |
+|        total_epoch      |    The number of epochs      |       1000      | Consistent with Global.epoch_num      |
--- a/doc/doc_en/customize_en.md
+++ b/doc/doc_en/customize_en.md
+# HOW TO MAKE YOUR OWN LIGHTWEIGHT OCR MODEL?
+
+The process of making a customized ultra-lightweight OCR models can be divided into three steps: training text detection model, training text recognition model, and concatenate the predictions from previous steps.
+
+## STEP1: TRAIN TEXT DETECTION MODEL
+
+PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
+```
+python3 tools/train.py -c configs/det/det_mv3_db.yml
+```
+For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
+
+## STEP2: TRAIN TEXT RECOGNITION MODEL
+
+PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
+```
+python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
+```
+For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)
+
+## STEP3: CONCATENATE PREDICTIONS
+
+PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
+
+When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recogniton model. The visualized results are saved to the `./inference_results` folder by default.
+
+```
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+For more details about text detection and recognition concatenation, please refer to the document [Inference](./inference_en.md)
--- a/doc/doc_en/datasets_en.md
+++ b/doc/doc_en/datasets_en.md
+## DATASET
+This is a collection of commonly used Chinese datasets, which is being updated continuously. You are welcome to contribute to this list～
+- [ICDAR2019-LSVT](#ICDAR2019-LSVT)
+- [ICDAR2017-RCTW-17](#ICDAR2017-RCTW-17)
+- [Chinese Street View Text Recognition](#中文街景文字识别)
+- [Chinese Document Text Recognition](#中文文档文字识别)
+- [ICDAR2019-ArT](#ICDAR2019-ArT)
+
+In addition to opensource data, users can also use synthesis tools to synthesize data themselves. Current available synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator), etc.
+
+<a name="ICDAR2019-LSVT"></a>
+#### 1. ICDAR2019-LSVT
+- **Data sources**：https://ai.baidu.com/broad/introduction?dataset=lsvt
+- **Introduction**： A total of 45w Chinese street view images, including 5w (2w test + 3w training) fully labeled data (text coordinates + text content), 40w weakly labeled data (text content only), as shown in the following figure:
+    ![](../datasets/LSVT_1.jpg)
+
+    (a) Fully labeled data
+
+    ![](../datasets/LSVT_2.jpg)
+    
+    (b) Weakly labeled data
+- **Download link**：https://ai.baidu.com/broad/download?dataset=lsvt
+
+<a name="ICDAR2017-RCTW-17"></a>
+#### 2. ICDAR2017-RCTW-17
+- **Data sources**：https://rctw.vlrlab.net/
+- **Introduction**：It contains 12000 + images, most of them are collected in the wild through mobile camera. Some are screenshots. These images show a variety of scenes, including street views, posters, menus, indoor scenes and screenshots of mobile applications.
+    ![](../datasets/rctw.jpg)
+- **Download link**：https://rctw.vlrlab.net/dataset/
+
+<a name="中文街景文字识别"></a>
+#### 3. Chinese Street View Text Recognition
+- **Data sources**：https://aistudio.baidu.com/aistudio/competition/detail/8
+- **Introduction**：A total of 290000 pictures are included, of which 210000 are used as training sets (with labels) and 80000 are used as test sets (without labels). The dataset is collected from the Chinese street view, and is formed by by cutting out the text line area (such as shop signs, landmarks, etc.) in the street view picture. All the images are preprocessed: by using affine transform, the text area is proportionally mapped to a picture with a height of 48 pixels, as shown in the figure:
+
+    ![](../datasets/ch_street_rec_1.png)  
+    (a) Label: 魅派集成吊顶  
+    ![](../datasets/ch_street_rec_2.png)  
+    (b) Label: 母婴用品连锁  
+- **Download link**
+https://aistudio.baidu.com/aistudio/datasetdetail/8429
+
+<a name="中文文档文字识别"></a>
+#### 4. Chinese Document Text Recognition
+- **Data sources**：https://github.com/YCG09/chinese_ocr  
+- **Introduction**：  
+    - A total of 3.64 million pictures are divided into training set and validation set according to 99:1.
+    - Using Chinese corpus (news + classical Chinese), the data is randomly generated through changes in font, size, grayscale, blur, perspective, stretching, etc.
+    - 5990 characters including Chinese characters, English letters, numbers and punctuation（Characters set: https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt ）
+    - Each sample is fixed with 10 characters, and the characters are randomly intercepted from the sentences in the corpus
+    - Image resolution is 280x32  
+    ![](../datasets/ch_doc1.jpg)  
+    ![](../datasets/ch_doc2.jpg)  
+    ![](../datasets/ch_doc3.jpg)  
+- **Download link**：https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (Password: lu7m)
+
+<a name="ICDAR2019-ArT"></a>
+#### 5、ICDAR2019-ArT
+- **Data source**：https://ai.baidu.com/broad/introduction?dataset=art
+- **Introduction**：It includes 10166 images, 5603 in training sets and 4563 in test sets. It is composed of three parts: total text, scut-ctw1500 and Baidu curved scene text, including text with various shapes such as horizontal, multi-directional and curved.
+    ![](../datasets/ArT.jpg)
+- **Download link**：https://ai.baidu.com/broad/download?dataset=art
--- a/doc/doc_en/detection_en.md
+++ b/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.
+
+## 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:
+```
+# Under the PaddleOCR path
+cd PaddleOCR/
+wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
+wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt
+```
+
+After decompressing the data set and downloading the annotation file, PaddleOCR/train_data/ has two folders and two files, which are:
+```
+/PaddleOCR/train_data/icdar2015/text_localization/
+  └─ icdar_c4_train_imgs/         Training data of icdar dataset
+  └─ ch4_test_images/             Testing data of icdar dataset
+  └─ train_icdar2015_label.txt    Training annotation of icdar dataset
+  └─ test_icdar2015_label.txt     Test annotation of icdar dataset
+```
+
+The provided annotation file format is as follow:
+```
+" 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.
+
+`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.
+
+
+## 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.
+```
+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
+# Download the pre-trained model of ResNet50
+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/
+
+# 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/
+  └─ conv_last_bn_mean
+  └─ conv_last_bn_offset
+  └─ conv_last_bn_scale
+  └─ conv_last_bn_variance
+  └─ ......
+
+```
+
+**START TRAINING**
+```
+python3 tools/train.py -c configs/det/det_mv3_db.yml
+```
+
+In the above instruction, use `-c` to select the training to use the configs/det/det_db_mv3.yml configuration file.
+For a detailed explanation of the configuration file, please refer to [link](./config_en.md).
+
+You can also use the `-o` parameter to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
+```
+python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
+```
+
+**load trained model and conntinue training**
+If you expect to load trained model and continue the training again, you can specify the `Global.checkpoints` parameter as the model path to be loaded.
+
+For example:
+```
+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 the priority 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
+
+PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.
+
+Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `det_db_mv3.yml`
+
+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.
+
+```
+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.
+
+Such as:
+```
+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
+```
+
+* Note: box_thresh and unclip_ratio are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
+
+## TEST DETECTION RESULT
+
+Test the detection result on a single image:
+```
+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"
+```
+
+When testing the DB model, adjust the post-processing threshold:
+```
+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
+```
+
+
+Test the detection result on all images in the folder:
+```
+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"
+```
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
+
+# PREDICTION FROM INFERENCE MODEL
+
+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.
+
+Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
+
+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 detection model to inference model
+
+Download the lightweight Chinese detection model:
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
+```
+The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
+```
+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/
+```
+When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file.
+`Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved.
+After the conversion is successful, there are two files in the `save_inference_dir` directory:
+```
+inference/det_db/
+  └─  model     Check the program file of inference model
+  └─  params    Check the parameter file of the inference model
+```
+
+### Convert recognition model to inference model
+
+Download the lightweight Chinese recognition model:
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
+```
+
+The recognition model is converted to the inference model in the same way as the detection, as follows:
+```
+python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
+        Global.save_inference_dir=./inference/rec_crnn/
+```
+
+If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
+
+After the conversion is successful, there are two files in the directory:
+```
+/inference/rec_crnn/
+  └─  model     Identify the saved model files
+  └─  params    Identify the parameter files of the inference model
+```
+
+## 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.
+
+### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
+
+For lightweight Chinese detection model inference, you can execute the following commands:
+
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"
+```
+
+The visual 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_2.jpg)
+
+By setting the size of the parameter `det_max_side_len`, the maximum value of picture normalization in the detection algorithm is changed. When the length and width of the picture are less than det_max_side_len, the original picture is used for prediction, otherwise the picture is scaled to the maximum value for prediction. This parameter is set to det_max_side_len=960 by default. If the resolution of the input picture is relatively large and you want to use a larger resolution for prediction, you can execute the following command:
+
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200
+```
+
+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
+```
+
+### 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:
+
+```
+# Set the yml configuration file of the training algorithm after -c
+# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# The Global.save_inference_dir parameter sets the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db"
+```
+
+DB text detection model inference, you can execute the following command:
+
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
+```
+
+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_db.jpg)
+
+**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.
+
+### 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:
+
+```
+# Set the yml configuration file of the training algorithm after -c
+# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# The Global.save_inference_dir parameter sets the address where the converted model will be saved.
+
+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:
+
+```
+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.
+
+
+## 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.
+
+
+### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
+
+For lightweight Chinese recognition model inference, you can execute the following commands:
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
+
+
+### 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`.
+
+First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)). It can be converted as follow:
+
+```
+# Set the yml configuration file of the training algorithm after -c
+# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# The Global.save_inference_dir parameter sets the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.checkpoints="./models/rec_r34_vd_tps_bilstm_ctc/best_accuracy" Global.save_inference_dir="./inference/starnet"
+```
+
+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"
+```
+
+### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
+![](../imgs_words_en/word_336.png)
+
+After executing the command, the recognition result of the above image is as follows:
+
+Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]
+
+**Note**：Since the above model refers to [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the training of lightweight Chinese recognition model in two aspects:
+
+- The image resolution used in training is different: the image resolution used in training the above model is [3，32，100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`.
+
+- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. Therefore, the parameter `rec_char_type` needs to be set during inference, which is specified as "en" in English.
+
+```
+self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+dict_character = list(self.character_str)
+```
+
+### 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.
+
+```
+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"
+```
+
+## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
+
+### 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.
+
+```
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
+```
+
+After executing the command, the recognition result image is as follows:
+
+![](../imgs_results/2.jpg)
+
+### 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:
+
+```
+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"
+```
+
+After executing the command, the recognition result image is as follows:
+
+![](../imgs_results/img_10.jpg)
--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
+## QUICK INSTALLATION
+
+After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
+
+PaddleOCR working environment:
+- PaddlePaddle 1.7+
+- python3
+- glibc 2.23
+- cuDNN 7.6+ (GPU)
+It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://docs.docker.com/get-started/).
+
+1. (Recommended) Prepare a docker environment. The first time you use this image, it will be downloaded automatically. Please be patient.
+```
+# Switch to the working directory
+cd /home/Projects
+# You need to create a docker container for the first run, and do not need to run the current command when you run it again
+# Create a docker container named ppocr and map the current directory to the /paddle directory of the container
+
+#If you want to use docker in a CPU environment, use docker instead of nvidia-docker to create docker
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
+```
+If you have cuda9 installed on your machine, please run the following command to create a container:
+```
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
+```
+If you have cuda10 installed on your machine, please run the following command to create a container:
+```
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev /bin/bash
+```
+You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get the image that fits your machine.
+```
+# ctrl+P+Q to exit docker, to re-enter docker using the following command:
+sudo docker container exec -it ppocr /bin/bash
+```
+
+Note: If the docker pull is too slow, you can download and load the docker image manually according to the following steps. Take cuda9 docker for example, you only need to change cuda9 to cuda10 to use cuda10 docker:
+```
+# Download the CUDA9 docker compressed file and unzip it
+wget https://paddleocr.bj.bcebos.com/docker/docker_pdocr_cuda9.tar.gz
+# To reduce download time, the uploaded docker image is compressed and needs to be decompressed
+tar zxf docker_pdocr_cuda9.tar.gz
+# Create image
+docker load < docker_pdocr_cuda9.tar
+# After completing the above steps, check whether the downloaded image is loaded through docker images
+docker images
+# If you have the following output after executing docker images, you can follow step 1 to create a docker environment.
+hub.baidubce.com/paddlepaddle/paddle   latest-gpu-cuda9.0-cudnn7-dev    f56310dcc829
+```
+
+2. Install PaddlePaddle Fluid v1.7
+```
+pip3 install --upgrade pip
+
+# If you have cuda9 installed on your machine, please run the following command to install
+python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# If you have cuda10 installed on your machine, please run the following command to install
+python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
+
+
+3. Clone PaddleOCR repo
+```
+# Recommend
+git clone https://github.com/PaddlePaddle/PaddleOCR
+
+# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
+
+git clone https://gitee.com/paddlepaddle/PaddleOCR
+
+# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
+```
+
+4. Install third-party libraries
+```
+cd PaddleOCR
+pip3 install -r requirments.txt
+```
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
+## TEXT RECOGNITION
+
+### DATA PREPARATION
+
+
+PaddleOCR supports two data formats: `LMDB` is used to train public data and evaluation algorithms; `general data` is used to train your own data:
+
+Please organize the dataset as follows:
+
+The default storage path for training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
+
+```
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
+```
+
+
+* Dataset download
+
+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
+
+* Use your own dataset:
+
+If you want to use your own data for training, please refer to the following to organize your data.
+
+- Training set
+
+First put the training images in the same folder (train_images), and use a txt file (rec_gt_train.txt) to store the image path and label.
+
+* Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error
+
+```
+" Image file name           Image annotation "
+
+train_data/train_0001.jpg   简单可依赖
+train_data/train_0002.jpg   用科技让复杂的世界更简单
+```
+PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
+
+```
+# Training set label
+wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
+# Test Set Label
+wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
+```
+
+The final training set should have the following file structure:
+
+```
+|-train_data
+    |-ic15_data
+        |- rec_gt_train.txt
+        |- train
+            |- word_001.png
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- Test set
+
+Similar to the training set, the test set also needs to be provided a folder containing all images (test) and a rec_gt_test.txt. The structure of the test set is as follows:
+
+```
+|-train_data
+    |-ic15_data
+        |- rec_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- Dictionary
+
+Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.
+
+Therefore, the dictionary needs to contain all the characters that you want to be recognized correctly. {word_dict_name}.txt needs to be written in the following format and saved in the `utf-8` encoding format:
+
+```
+l
+d
+a
+d
+r
+n
+```
+
+In `word_dict.txt`, there is a single word in each line, which maps characters and numeric indexes together, e.g "and" will be mapped to [2 5 1]
+
+`ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.
+
+`ppocr/utils/ic15_dict.txt` is an English dictionary with 36 characters.
+
+You can use them if needed.
+
+To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
+
+### TRAINING
+
+PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
+
+First download the pretrain model, you can download the trained model to finetune on the icdar2015 data:
+
+```
+cd PaddleOCR/
+# Download the pre-trained model of MobileNetV3
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar
+# Decompress model parameters
+cd pretrain_models
+tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
+```
+
+Start training:
+
+```
+# Set PYTHONPATH path
+export PYTHONPATH=$PYTHONPATH:.
+# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+# Training icdar15 English data
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
+```
+
+PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process.
+
+If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
+
+* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:
+
+
+| Configuration file |  Algorithm |   backbone |   trans   |   seq      |     pred     |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   |
+| rec_chinese_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
+| rec_icdar15_train.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
+| rec_mv3_none_bilstm_ctc.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
+| rec_mv3_none_none_ctc.yml |  Rosetta |   Mobilenet_v3 large 0.5 |  None   |  None |  ctc  |
+| rec_mv3_tps_bilstm_ctc.yml |  STARNet |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  ctc  |
+| rec_mv3_tps_bilstm_attn.yml |  RARE |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  attention  |
+| rec_r34_vd_none_bilstm_ctc.yml |  CRNN |   Resnet34_vd |  None   |  BiLSTM |  ctc  |
+| 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 |
+
+For training Chinese data, it is recommended to use `rec_chinese_lite_train.yml`. If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
+co
+Take `rec_mv3_none_none_ctc.yml` as an example:
+```
+Global:
+  ...
+  # Modify image_shape to fit long text
+  image_shape: [3, 32, 320]
+  ...
+  # Modify character type
+  character_type: ch
+  # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
+  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
+  ...
+  # Modify reader type
+  reader_yml: ./configs/rec/rec_chinese_reader.yml
+  # Whether to use data augmentation
+  distort: true
+  # Whether to recognize spaces
+  use_space_char: true
+  ...
+
+...
+
+Optimizer:
+  ...
+  # Add learning rate decay strategy
+  decay:
+    function: cosine_decay
+    # Each epoch contains iter number
+    step_each_epoch: 20
+    # Total epoch number
+    total_epoch: 1000
+```
+**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+
+
+### EVALUATION
+
+The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
+
+```
+export CUDA_VISIBLE_DEVICES=0
+# GPU evaluation, Global.checkpoints is the weight to be tested
+python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+### PREDICTION
+
+* Training engine prediction
+
+Using the model trained by paddleocr, you can quickly get prediction through the following script.
+
+The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
+
+```
+# Predict English results
+python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+```
+
+Input image:
+
+![](../imgs_words/en/word_1.png)
+
+Get the prediction result of the input image:
+
+```
+infer_img: doc/imgs_words/en/word_1.png
+     index: [19 24 18 23 29]
+     word : joint
+```
+
+The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml`, you can use the following command to predict the Chinese model:
+
+```
+# Predict Chinese results
+python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
+```
+
+Input image:
+
+![](../imgs_words/ch/word_1.jpg)
+
+Get the prediction result of the input image:
+
+```
+infer_img: doc/imgs_words/ch/word_1.jpg
+     index: [2092  177  312 2503]
+     word : 韩国小馆
+```