fix TRT8 core bug

03bb378f · LDOUBLEV · a2a12fe4 · 2e9abcb9 · 03bb378f · 03bb378f
Commit 03bb378f authored Nov 25, 2021 by LDOUBLEV
20 changed files
--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
@@ -47,10 +47,10 @@ cd /path/to/ppocr_img
 <a name="211"></a>
 #### 2.1.1 中英文模型
-* 检测+方向分类器+识别全流程：设置方向分类器参数`--use_angle_cls true`后可对竖排文本进行识别。
+* 检测+方向分类器+识别全流程：`--use_angle_cls true`设置使用方向分类器识别180度旋转文字，`--use_gpu false`设置不使用GPU
  ```bash
-  paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
+  paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true --use_gpu false
  ```
  结果是一个list，每个item包含了文本框，文字和识别置信度

--- a/doc/doc_ch/reference.md
+++ b/doc/doc_ch/reference.md
@@ -112,4 +112,14 @@
  year={2016}
 }
+13.NRTR
+@misc{sheng2019nrtr,
+      title={NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
+      author={Fenfen Sheng and Zhineng Chen and Bo Xu},
+      year={2019},
+      eprint={1806.00926},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
 ```
--- a/doc/doc_ch/thirdparty.md
+++ b/doc/doc_ch/thirdparty.md
+# 社区贡献说明
+感谢大家长久以来对PaddleOCR的支持和关注，与广大开发者共同构建一个专业、和谐、相互帮助的开源社区是PaddleOCR的目标。本文档展示了已有的社区贡献、对于各类贡献说明、新的机会与流程，希望贡献流程更加高效、路径更加清晰。
+PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实现自己的想法，享受创造价值带来的愉悦。
+<a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=PaddlePaddle/PaddleOCR" />
+</a>
+> 上图为PaddleOCR目前的Contributor，定期更新
+## 1. 社区贡献
+### 1.1 为PaddleOCR新增功能
+- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android([#340](https://github.com/PaddlePaddle/PaddleOCR/pull/340))和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码([#325](https://github.com/PaddlePaddle/PaddleOCR/pull/325))
+- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务([#507](https://github.com/PaddlePaddle/PaddleOCR/pull/507))。
+- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用([#1027](https://github.com/PaddlePaddle/PaddleOCR/pull/1027))。
+- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了[PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/README_ch.md) 的完整代码。
+### 1.2 基于PaddleOCR的社区贡献
+- 【最新】完整的C#版本标注工具 [FastOCRLabel](https://gitee.com/BaoJianQiang/FastOCRLabel) (@ [包建强](https://gitee.com/BaoJianQiang) )
+- 通用型桌面级即时翻译工具 [DangoOCR离线版](https://github.com/PantsuDango/DangoOCR) (@ [PantsuDango](https://github.com/PantsuDango))
+- 获取OCR识别结果的key-value [paddleOCRCorrectOutputs](https://github.com/yuranusduke/paddleOCRCorrectOutputs) (@ [yuranusduke](https://github.com/yuranusduke))
+- 截屏转文字工具  [scr2txt](https://github.com/lstwzd/scr2txt) (@ [lstwzd](https://github.com/lstwzd))
+- 身份证复印件识别 [id_card_ocr](https://github.com/baseli/id_card_ocr)(@ [baseli](https://github.com/baseli))
+- 能看懂表格图片的数据助手：[Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader) (@ [thunder95][https://github.com/thunder95])
+- 英文视频自动生成字幕 [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/1054614?channelType=0&channel=0)( @ [叶月水狐](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/322052))
+### 1.3 代码与文档优化
+- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题([#210](https://github.com/PaddlePaddle/PaddleOCR/pull/210))。
+- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码([#304](https://github.com/PaddlePaddle/PaddleOCR/pull/304))。
+- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格([so many commits)](https://github.com/PaddlePaddle/PaddleOCR/commits?author=BeyondYourself)。
+- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档。
+### 1.4 多语言语料
+- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集([#321](https://github.com/PaddlePaddle/PaddleOCR/pull/321))。
+- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料([#954](https://github.com/PaddlePaddle/PaddleOCR/pull/954))。
+## 2. 贡献说明
+### 2.1 新增功能类
+PaddleOCR非常欢迎社区贡献以PaddleOCR为核心的各种服务、部署实例与软件应用，经过认证的社区贡献会被添加在上述社区贡献表中，为广大开发者增加曝光，也是PaddleOCR的荣耀，其中：
+- 项目形式：官方社区认证的项目代码应有良好的规范和结构，同时，还应配备一个详细的README.md，说明项目的使用方法。通过在requirements.txt文件中增加一行 `paddleocr` 可以自动收录到PaddleOCR的usedby中。
+- 合入方式：如果是对PaddleOCR现有工具的更新升级，则会合入主repo。如果为PaddleOCR拓展了新功能，请先与官方人员联系，确认项目是否合入主repo，*即使新功能未合入主repo，我们同样也会以社区贡献的方式为您的个人项目增加曝光。*
+### 2.2 代码优化
+如果您在使用PaddleOCR时遇到了代码bug、功能不符合预期等问题，可以为PaddleOCR贡献您的修改，其中：
+- Python代码规范可参考[附录1：Python代码规范](./code_and_doc.md/#附录1)。
+-  提交代码前请再三确认不会引入新的bug，并在PR中描述优化点。如果该PR解决了某个issue，请在PR中连接到该issue。所有的PR都应该遵守附录3中的[3.2.10 提交代码的一些约定。](./code_and_doc.md/#提交代码的一些约定)
+- 请在提交之前参考下方的[附录3：Pull Request说明](./code_and_doc.md/#附录3)。如果您对git的提交流程不熟悉，同样可以参考附录3的3.2节。
+**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理**。
+### 2.3 文档优化
+如果您在使用PaddleOCR时遇到了文档表述不清楚、描述缺失、链接失效等问题，可以为PaddleOCR贡献您的修改。文档书写规范请参考[附录2：文档规范](./code_and_doc.md/#附录2)。**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
+## 3. 更多贡献机会
+我们非常鼓励开发者使用PaddleOCR实现自己的想法，同时我们也列出一些经过分析后认为有价值的拓展方向，供大家参考
+- 功能类：IOS端侧demo、前后处理工具、针对各种垂类场景的检测识别模型（如手写体、公式）。
+- 文档类：PaddleOCR在各种垂类行业的应用案例（可在公众号中推广）。
+## 4. 联系我们
+PaddleOCR非常欢迎广大开发者在有意向贡献前与我们联系，这样可以大大降低PR过程中的沟通成本。同时，如果您觉得某些想法个人难以实现，我们也可以通过SIG的形式定向为项目招募志同道合的开发者一起共建。通过SIG渠道贡献的项目将会获得深层次的研发支持与运营资源。
+我们推荐的贡献流程是：
+- 通过在github issue的题目中增加  `【third-party】` 标记，说明遇到的问题（以及解决的思路）或想拓展的功能，等待值班人员回复。例如 `【third-party】为PaddleOCR贡献IOS示例`
+- 与我们沟通确认技术方案或bug、优化点准确无误后进行功能新增或相应的修改，代码与文档遵循相关规范。
+- PR链接到上述issue，等待review。
+## 5. 致谢与后续
+  - 合入代码之后，首页README末尾新增感谢贡献，默认链接为github名字及主页，如果有需要更换主页，也可以联系我们。
+  - 新增重要功能类，会在用户群广而告之，享受开源社区荣誉时刻。
+  - **如果您有基于PaddleOCR的贡献，但未出现在上述列表中，请按照 `4. 联系我们` 的步骤与我们联系。**
--- a/doc/doc_ch/training.md
+++ b/doc/doc_ch/training.md
-# 模型训练
+# PP-OCR模型训练
 本文将介绍模型训练时需掌握的基本概念，和训练时的调优方法。
 同时会简单介绍PaddleOCR模型训练数据的组成部分，以及如何在垂类场景中准备数据finetune模型。
- [1. 基本概念](#基本概念)
+- [1.配置文件说明](#配置文件)
-  * [1.1 学习率](#学习率)
+- [2. 基本概念](#基本概念)
-  * [1.2 正则化](#正则化)
+  * [2.1 学习率](#学习率)
-  * [1.3 评估指标](#评估指标)
+  * [2.2 正则化](#正则化)
- [2. 数据与垂类场景](#数据与垂类场景)
+  * [2.3 评估指标](#评估指标)
-  * [2.1 训练数据](#训练数据)
+- [3. 数据与垂类场景](#数据与垂类场景)
-  * [2.2 垂类场景](#垂类场景)
+  * [3.1 训练数据](#训练数据)
-  * [2.3 自己构建数据集](#自己构建数据集)
+  * [3.2 垂类场景](#垂类场景)
-* [3. 常见问题](#常见问题)
+  * [3.3 自己构建数据集](#自己构建数据集)
+* [4. 常见问题](#常见问题)
+<a name="配置文件"></a>
+## 1. 配置文件说明
+PaddleOCR模型使用配置文件管理网络训练、评估的参数。在配置文件中，可以设置组建模型、优化器、损失函数、模型前后处理的参数，PaddleOCR从配置文件中读取到这些参数，进而组建出完整的训练流程，完成模型训练，在需要对模型进行优化的时，可以通过修改配置文件中的参数完成配置，使用简单且方便修改。
+完整的配置文件说明可以参考[配置文件](./config.md)
 <a name="基本概念"></a>
-## 1. 基本概念
-OCR(Optical Character Recognition,光学字符识别)是指对图像进行分析识别处理，获取文字和版面信息的过程，是典型的计算机视觉任务，
+## 2. 基本概念
-通常由文本检测和文本识别两个子任务构成。
-模型调优时需要关注以下参数：
+模型训练过程中需要手动调整一些超参数，帮助模型以最小的代价获得最优指标。不同的数据量可能需要不同的超参，当您希望在自己的数据上finetune或对模型效果调优时，有以下几个参数调整策略可供参考：
 <a name="学习率"></a>
-### 1.1 学习率
+### 2.1 学习率
 学习率是训练神经网络的重要超参数之一，它代表在每一次迭代中梯度向损失函数最优解移动的步长。
 在PaddleOCR中提供了多种学习率更新策略,可以通过配置文件修改，例如：
@@ -42,7 +49,7 @@ Piecewise 代表分段常数衰减，在不同的学习阶段指定不同的学
 warmup_epoch 代表在前5个epoch中，学习率将逐渐从0增加到base_lr。全部策略可以参考代码[learning_rate.py](../../ppocr/optimizer/learning_rate.py) 。
 <a name="正则化"></a>
-### 1.2 正则化
+### 2.2 正则化
 正则化可以有效的避免算法过拟合，PaddleOCR中提供了L1、L2正则方法，L1 和 L2 正则化是最常用的正则化方法。L1 正则化向目标函数添加正则化项，以减少参数的绝对值总和；而 L2 正则化中，添加正则化项的目的在于减少参数平方的总和。配置方法如下：
@@ -55,7 +62,7 @@ Optimizer:
 ```
 <a name="评估指标"></a>
-### 1.3 评估指标
+### 2.3 评估指标
 （1）检测阶段：先按照检测框和标注框的IOU评估，IOU大于某个阈值判断为检测准确。这里检测框和标注框不同于一般的通用目标检测框，是采用多边形进行表示。检测准确率：正确的检测框个数在全部检测框的占比，主要是判断检测指标。检测召回率：正确的检测框个数在全部标注框的占比，主要是判断漏检的指标。
@@ -65,10 +72,10 @@ Optimizer:
 <a name="数据与垂类场景"></a>
-## 2. 数据与垂类场景
+## 3. 数据与垂类场景
 <a name="训练数据"></a>
-### 2.1 训练数据
+### 3.1 训练数据
 目前开源的模型，数据集和量级如下：
    - 检测：  
@@ -83,13 +90,14 @@ Optimizer:
 其中，公开数据集都是开源的，用户可自行搜索下载，也可参考[中文数据集](./datasets.md)，合成数据暂不开源，用户可使用开源合成工具自行合成，可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer) 、[SynthText](https://github.com/ankush-me/SynthText) 、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) 等。
 <a name="垂类场景"></a>
-### 2.2 垂类场景
+### 3.2 垂类场景
 PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练；
 如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。
 <a name="自己构建数据集"></a>
-### 2.3 自己构建数据集
+### 3.3 自己构建数据集
 在构建数据集时有几个经验可供参考：
@@ -107,7 +115,7 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
 <a name="常见问题"></a>
-## 3. 常见问题
+## 4. 常见问题
 **Q**：训练CRNN识别时，如何选择合适的网络输入shape？
@@ -130,8 +138,13 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
    A：识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
 ***
 具体的训练教程可点击下方链接跳转： 
- [文本检测模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)  
- [文本识别模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+\- [文本检测模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md) 
- [文本方向分类器训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  
\ No newline at end of file
+\- [文本识别模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+\- [文本方向分类器训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  
--- a/doc/doc_ch/whl.md
+++ b/doc/doc_ch/whl.md
@@ -420,3 +420,5 @@ im_show.save('result.jpg')
 | cls                     | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类)                                                                                                                                                                                                | FALSE                    |
 | show_log                     | 是否打印det和rec等信息                                                                                                                                                                                                | FALSE                    |
 | type                     | 执行ocr或者表格结构化, 值可选['ocr','structure']                                                                                                                                                                                             | ocr                    |
+| ocr_version                     | OCR模型版本，可选PP-OCRv2, PP-OCR。PP-OCRv2 目前仅支持中文的检测和识别模型，PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型                                                                                                                                        | PP-OCRv2                   |
+| structure_version                     | 表格结构化模型版本，可选 STRUCTURE。STRUCTURE支持表格结构化模型                                                                                                                                                                                        | STRUCTURE                    |
--- a/doc/doc_en/add_new_algorithm_en.md
+++ b/doc/doc_en/add_new_algorithm_en.md
-# Add new algorithm
+# Add New Algorithm
 PaddleOCR decomposes an algorithm into the following parts, and modularizes each part to make it more convenient to develop new algorithms.
@@ -263,7 +263,7 @@ Metric:
  main_indicator: acc
 ```
-## 优化器
+## Optimizer
 The optimizer is used to train the network. The optimizer also contains network regularization and learning rate decay modules. This part is under [ppocr/optimizer](../../ppocr/optimizer). PaddleOCR has built-in
 Commonly used optimizer modules such as `Momentum`, `Adam` and `RMSProp`, common regularization modules such as `Linear`, `Cosine`, `Step` and `Piecewise`, and common learning rate decay modules such as `L1Decay` and `L2Decay`.

--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
+# Two-stage Algorithm
+- [1. Algorithm Introduction](#1-algorithm-introduction)
+  * [1.1 Text Detection Algorithm](#11-text-detection-algorithm)
+  * [1.2 Text Recognition Algorithm](#12-text-recognition-algorithm)
+- [2. Training](#2-training)
+- [3. Inference](#3-inference)
 <a name="Algorithm_introduction"></a>
-## Algorithm introduction
+## 1. Algorithm Introduction
 This tutorial lists the text detection algorithms and text recognition algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).
@@ -8,31 +17,32 @@ This tutorial lists the text detection algorithms and text recognition algorithm
 - [2. Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
 <a name="TEXTDETECTIONALGORITHM"></a>
-### 1. Text Detection Algorithm
+### 1.1 Text Detection Algorithm
 PaddleOCR open source text detection algorithms list:
- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[2]
- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
+- [x]  DB([paper](https://arxiv.org/abs/1911.08947))[1]
- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
- [x]  PSE([paper](https://arxiv.org/abs/1903.12473v2))
+- [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
 On the ICDAR2015 dataset, the text detection result is as follows:
-|Model|Backbone|precision|recall|Hmean|Download link|
+|Model|Backbone|Precision|Recall|Hmean|Download link|
 | --- | --- | --- | --- | --- | --- |
-|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|MobileNetV3|79.42%|80.64%|80.03%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
+|EAST|MobileNetV3|79.42%|80.64%|80.03%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
-|DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|ResNet50_vd|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
-|DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|DB|MobileNetV3|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
-|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
-|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
-|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 On Total-Text dataset, the text detection result is as follows:
-|Model|Backbone|precision|recall|Hmean|Download link|
+|Model|Backbone|Precision|Recall|Hmean|Download link|
 | --- | --- | --- | --- | --- | --- |
-|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
+|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 **Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
 * [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
@@ -41,31 +51,41 @@ On Total-Text dataset, the text detection result is as follows:
 For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./detection_en.md)
 <a name="TEXTRECOGNITIONALGORITHM"></a>
-### 2. Text Recognition Algorithm
+### 1.2 Text Recognition Algorithm
 PaddleOCR open-source text recognition algorithms list:
- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
- [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
+- [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))[13]
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
+- [x] SEED([paper](https://arxiv.org/pdf/2005.10977.pdf))
 Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
 |Model|Backbone|Avg Accuracy|Module combination|Download link|
 |---|---|---|---|---|
-|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
+|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
-|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
+|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
-|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
+|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
-|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
+|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
-|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
+|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
-|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
-|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
-|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
-|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
+|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
-|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
+|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
-|SAR|Resnet31| 87.2% | rec_r31_sar | [Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
+|SAR|Resnet31| 87.2% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
+|SEED|Aster_Resnet| 85.2% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
-Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
+Please refer to the document for training guide and use of PaddleOCR 
+## 2. Training
+For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./detection_en.md). For text recognition algorithms, please refer to [Text recognition model training/evaluation/prediction](./recognition_en.md)
+## 3. Inference
+Except for the PP-OCR series models of the above models, the other models only support inference based on the Python engine. For details, please refer to [Inference based on Python prediction engine](./inference_en.md)
--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
@@ -15,7 +15,6 @@ The text line image obtained after text detection is sent to the recognition mod
 Example of 0 and 180 degree data samples：
 ![](../imgs_results/angle_class_example.jpg)
-### DATA PREPARATION
 <a name="data-preparation"></a>
 ## 2. Data Preparation
@@ -131,8 +130,6 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
 <a name="prediction"></a>
 ## 5. Prediction
-### PREDICTION
 * Training engine prediction
 Using the model trained by paddleocr, you can quickly get prediction through the following script.

--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
@@ -36,10 +36,10 @@ Take rec_chinese_lite_train_v2.0.yml as an example
 |      pretrained_model    |    Set the path of the pre-trained model      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
 |      checkpoints         |    set model parameter path            |       None        |   Used to load parameters after interruption to continue training|
 |      use_visualdl  |    Set whether to enable visualdl for visual log display |          False        |    [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
-|      infer_img            |    Set inference image path or folder path     |       ./infer_img | \|
+|      infer_img            |    Set inference image path or folder path     |       ./infer_img | \||
 |      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  | If the character_dict_path is None, model can only recognize number and lower letters |
 |      max_text_length     |    Set the maximum length of text        |       25          |                \                 |
-|      use_space_char     |    Set whether to recognize spaces             |        True      |          Only support in character_type=ch mode                 |
+|      use_space_char     |    Set whether to recognize spaces             |        True      |          \|               |
 |      label_list          |    Set the angle supported by the direction classifier       |    ['0','180']    |     Only valid in angle classifier model |
 |      save_res_path          |    Set the save address of the test model results       |    ./output/det_db/predicts_db.txt    |     Only valid in the text detection model |

--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -99,6 +99,18 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o   \
 # Set the GPU ID used by the '--gpus' parameter.
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+# multi-Node, multi-GPU training
+# Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter.
+python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+```
+**Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. In addition, it requires activating commands separately on multiple machines when we start the training. The command for viewing the IP address of the machine is `ifconfig`.
+If you want to further speed up the training, you can use [automatic mixed precision training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows:
+```
+python3 tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
+     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
 ```
 ### 2.2 Load Trained Model and Continue Training
@@ -223,6 +235,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./out
 ## 5. FAQ
 Q1: The prediction results of trained model and inference model are inconsistent?
 **A**: Most of the problems are caused by the inconsistency of the pre-processing and post-processing parameters during the prediction of the trained model and the pre-processing and post-processing parameters during the prediction of the inference model. Taking the model trained by the det_mv3_db.yml configuration file as an example, the solution to the problem of inconsistent prediction results between the training model and the inference model is as follows:
 - Check whether the [trained model preprocessing](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116) is consistent with the prediction [preprocessing function of the inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42). When the algorithm is evaluated, the input image size will affect the accuracy. In order to be consistent with the paper, the image is resized to [736, 1280] in the training icdar15 configuration file, but there is only a set of default parameters when the inference model predicts, which will be considered To predict the speed problem, the longest side of the image is limited to 960 for resize by default. The preprocessing function of the training model preprocessing and the inference model is located in [ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
 - Check whether the [post-processing of the trained model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51) is consistent with the [post-processing parameters of the inference](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50).
--- a/doc/doc_en/environment_en.md
+++ b/doc/doc_en/environment_en.md
 # Environment Preparation
+Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
 Recommended working environment:
 - PaddlePaddle >= 2.0.0 (2.1.2)
 - python3.7

--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
-# Python Inference for PP-OCR Model Library
+# Python Inference for PP-OCR Model Zoo
 This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
 - [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
 - [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
    - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
    - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
 - [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
 - [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
 <a name="DETECTION_MODEL_INFERENCE"></a>
@@ -22,10 +19,10 @@ The default configuration is based on the inference setting of the DB text detec
 ```
 # download DB text detection inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
-# predict
+# run inference
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
 ```
 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:
@@ -42,12 +39,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
 If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 If you want to use the CPU for prediction, execute the command as follows
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 <a name="RECOGNITION_MODEL_INFERENCE"></a>
@@ -62,9 +59,10 @@ For lightweight Chinese recognition model inference, you can execute the followi
 ```
 # download CRNN text recognition inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+# run inference
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 ![](../imgs_words_en/word_10.png)
@@ -78,10 +76,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
 ### 2. Multilingaul Model Inference
-If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
+If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
 ```
+wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
@@ -120,13 +120,13 @@ When performing prediction, you need to specify the path of a single image or a
 ```shell
 # use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 # not use use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 # use multi-process
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```

--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
-## OCR model list（V2.1, updated on 2021.9.6）
+# OCR Model List（V2.1, updated on 2021.9.6）
 > **Note**
 > 1. Compared with the model v2.0, the 2.1 version of the detection model has a improvement in accuracy, and the 2.1 version of the recognition model is optimized in accuracy and CPU speed.
 > 2. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
@@ -6,9 +6,9 @@
 - [1. Text Detection Model](#Detection)
 - [2. Text Recognition Model](#Recognition)
-    - [Chinese Recognition Model](#Chinese)
+    - [2.1 Chinese Recognition Model](#Chinese)
-    - [English Recognition Model](#English)
+    - [2.2 English Recognition Model](#English)
-    - [Multilingual Recognition Model](#Multilingual)
+    - [2.3 Multilingual Recognition Model](#Multilingual)
 - [3. Text Angle Classification Model](#Angle)
 - [4. Paddle-Lite Model](#Paddle-Lite)
@@ -25,26 +25,26 @@ Relationship of the above models is as follows.
 ![](../imgs_en/model_prod_flow_en.png)
 <a name="Detection"></a>
-### 1. Text Detection Model
+## 1. Text Detection Model
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
-|ch_PP-OCRv2_det_slim|slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
+|ch_PP-OCRv2_det_slim|[New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
-|ch_PP-OCRv2_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
+|ch_PP-OCRv2_det|[New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
 |ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|2.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
 |ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
 |ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
 <a name="Recognition"></a>
-### 2. Text Recognition Model
+## 2. Text Recognition Model
 <a name="Chinese"></a>
-#### Chinese Recognition Model
+### 2.1 Chinese Recognition Model
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
-|ch_PP-OCRv2_rec_slim|Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
+|ch_PP-OCRv2_rec_slim|[New] Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
-|ch_PP-OCRv2_rec|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
+|ch_PP-OCRv2_rec|[New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
 |ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
 |ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
 |ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
@@ -53,7 +53,7 @@ Relationship of the above models is as follows.
 **Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
 <a name="English"></a>
-#### English Recognition Model
+### 2.2 English Recognition Model
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
@@ -61,7 +61,7 @@ Relationship of the above models is as follows.
 |en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
 <a name="Multilingual"></a>
-#### Multilingual Recognition Model（Updating...）
+### 2.3 Multilingual Recognition Model（Updating...）
 |model name| dict file | description|config|model size|download|
 | --- | --- | --- |--- | --- | --- |
@@ -82,7 +82,7 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
 <a name="Angle"></a>
-### 3. Text Angle Classification Model
+## 3. Text Angle Classification Model
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
@@ -90,7 +90,7 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
 |ch_ppocr_mobile_v2.0_cls|Original model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
 <a name="Paddle-Lite"></a>
-### 4. Paddle-Lite Model
+## 4. Paddle-Lite Model
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
 |PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|

--- a/doc/doc_en/pgnet_en.md
+++ b/doc/doc_en/pgnet_en.md
@@ -24,9 +24,9 @@ The results of detection and recognition are as follows:
 ![](../imgs_results/e2e_res_img293_pgnet.png)
 ![](../imgs_results/e2e_res_img295_pgnet.png)
 ### Performance
-####Test set: Total Text
+#### Test set: Total Text
-####Test environment: NVIDIA Tesla V100-SXM2-16GB
+#### Test environment: NVIDIA Tesla V100-SXM2-16GB
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|download|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 |Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
@@ -36,7 +36,7 @@ The results of detection and recognition are as follows:
 <a name="Environment_Configuration"></a>
 ## 2. Environment Configuration
-Please refer to [Quick Installation](./installation_en.md) Configure the PaddleOCR running environment.
+Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [PaddleOCR Overview and Project Clone](./paddleOCR_overview_en.md) to clone the project
 <a name="Quick_Use"></a>
 ## 3. Quick Use

--- a/doc/doc_en/quickstart_en.md
+++ b/doc/doc_en/quickstart_en.md
@@ -53,10 +53,10 @@ If you do not use the provided test image, you can replace the following `--imag
 #### 2.1.1 Chinese and English Model
-* Detection, direction classification and recognition: set the direction classifier parameter`--use_angle_cls true` to recognize vertical text.
+* Detection, direction classification and recognition: set the parameter`--use_gpu false` to disable the gpu device
  ```bash
-  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en
+  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false
  ```
  Output will be a list, each item contains bounding box, text and recognition confidence

--- a/doc/doc_en/training_en.md
+++ b/doc/doc_en/training_en.md
@@ -25,11 +25,10 @@ The PaddleOCR model uses configuration files to manage network training and eval
 For the complete configuration file description, please refer to [Configuration File](./config_en.md)
 <a name="1-basic-concepts"></a>
-# 1. Basic concepts
 ## 2. Basic Concepts
-The following parameters need to be paid attention to when tuning the model:
+In the process of model training, some hyperparameters need to be manually adjusted to help the model obtain the optimal index at the least loss. Different data volumes may require different hyper-parameters. When you want to finetune your own data or tune the model effect, there are several parameter adjustment strategies for reference:
 <a name="11-learning-rate"></a>
 ### 2.1 Learning Rate
@@ -53,7 +52,7 @@ and the learning rate is the same in each stage.
 warmup_epoch means that in the first 5 epochs, the learning rate will gradually increase from 0 to base_lr. For all strategies, please refer to the code [learning_rate.py](../../ppocr/optimizer/learning_rate.py).
 <a name="12-regularization"></a>
-## 1.2 Regularization
+### 2.2 Regularization
 Regularization can effectively avoid algorithm overfitting. PaddleOCR provides L1 and L2 regularization methods.
 L1 and L2 regularization are the most commonly used regularization methods.
@@ -125,7 +124,7 @@ There are several experiences for reference when constructing the data set:
 <a name="3-faq"></a>
-# 3. FAQ
+## 4. FAQ
 **Q**: How to choose a suitable network input shape when training CRNN recognition?
@@ -147,9 +146,10 @@ There are several experiences for reference when constructing the data set:
    A: It is normal for the acc to be 0 at the beginning of the recognition model training, and the indicator will come up after a longer training period.
 ***
 Click the following links for detailed training tutorial:  
 - [text detection model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)  
 - [text recognition model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
 - [text direction classification model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  
--- a/doc/doc_en/whl_en.md
+++ b/doc/doc_en/whl_en.md
@@ -367,3 +367,5 @@ im_show.save('result.jpg')
 | cls                     | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction)                                                                                                                                                                                                   | FALSE                    |
 | show_log                     | Whether to print log in det and rec | FALSE                    |
 | type                     | Perform ocr or table structuring, the value is selected in ['ocr','structure']                                                                                                                                                                                             | ocr                    |
+| ocr_version                     | OCR Model version number, the current model support list is as follows: PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv2                 |
+| structure_version                     | table structure Model version number, the current model support list is as follows: STRUCTURE support english table structure model | STRUCTURE                 |
--- a/doc/joinus.PNG
+++ b/doc/joinus.PNG
--- a/doc/pr.png
+++ b/doc/pr.png
--- a/paddleocr.py
+++ b/paddleocr.py
@@ -16,6 +16,9 @@ import os
 import sys
 __dir__ = os.path.dirname(__file__)
+import paddle
 sys.path.append(os.path.join(__dir__, ''))
 import cv2
@@ -29,7 +32,7 @@ from ppocr.utils.logging import get_logger
 logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
 from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
-from tools.infer.utility import draw_ocr, str2bool
+from tools.infer.utility import draw_ocr, str2bool, check_gpu
 from ppstructure.utility import init_args, draw_structure_result
 from ppstructure.predict_system import OCRSystem, save_structure_res
@@ -39,13 +42,15 @@ __all__ = [
 ]
 SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.2.1'
+VERSION = '2.3.0.2'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
-DEFAULT_MODEL_VERSION = '2.0'
+DEFAULT_OCR_MODEL_VERSION = 'PP-OCR'
+DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE'
 MODEL_URLS = {
-    '2.1': {
+    'OCR': {
+        'PP-OCRv2': {
            'det': {
                'ch': {
                    'url':
@@ -60,7 +65,7 @@ MODEL_URLS = {
                }
            }
        },
-    '2.0': {
+        DEFAULT_OCR_MODEL_VERSION: {
            'det': {
                'ch': {
                    'url':
@@ -158,6 +163,10 @@ MODEL_URLS = {
                    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
                }
            },
+        }
+    },
+    'STRUCTURE': {
+        DEFAULT_STRUCTURE_MODEL_VERSION: {
            'table': {
                'en': {
                    'url':
@@ -166,6 +175,7 @@ MODEL_URLS = {
                }
            }
        }
+    }
 }
@@ -177,7 +187,20 @@ def parse_args(mMain=True):
    parser.add_argument("--det", type=str2bool, default=True)
    parser.add_argument("--rec", type=str2bool, default=True)
    parser.add_argument("--type", type=str, default='ocr')
-    parser.add_argument("--version", type=str, default='2.1')
+    parser.add_argument(
+        "--ocr_version",
+        type=str,
+        default='PP-OCRv2',
+        help='OCR Model version, the current model support list is as follows: '
+        '1. PP-OCRv2 Support Chinese detection and recognition model. '
+        '2. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
+    )
+    parser.add_argument(
+        "--structure_version",
+        type=str,
+        default='STRUCTURE',
+        help='Model version, the current model support list is as follows:'
+        ' 1. STRUCTURE Support en table structure model.')
    for action in parser._actions:
        if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
@@ -215,9 +238,9 @@ def parse_lang(lang):
        lang = "cyrillic"
    elif lang in devanagari_lang:
        lang = "devanagari"
-    assert lang in MODEL_URLS[DEFAULT_MODEL_VERSION][
+    assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][
        'rec'], 'param lang must in {}, but got {}'.format(
-            MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang)
+            MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang)
    if lang == "ch":
        det_lang = "ch"
    elif lang == 'structure':
@@ -227,33 +250,41 @@ def parse_lang(lang):
    return lang, det_lang
-def get_model_config(version, model_type, lang):
+def get_model_config(type, version, model_type, lang):
-    if version not in MODEL_URLS:
+    if type == 'OCR':
-        logger.warning('version {} not in {}, use version {} instead'.format(
+        DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION
-            version, MODEL_URLS.keys(), DEFAULT_MODEL_VERSION))
+    elif type == 'STRUCTURE':
+        DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION
+    else:
+        raise NotImplementedError
+    model_urls = MODEL_URLS[type]
+    if version not in model_urls:
+        logger.warning('version {} not in {}, auto switch to version {}'.format(
+            version, model_urls.keys(), DEFAULT_MODEL_VERSION))
        version = DEFAULT_MODEL_VERSION
-    if model_type not in MODEL_URLS[version]:
+    if model_type not in model_urls[version]:
-        if model_type in MODEL_URLS[DEFAULT_MODEL_VERSION]:
+        if model_type in model_urls[DEFAULT_MODEL_VERSION]:
            logger.warning(
-                'version {} not support {} models, use version {} instead'.
+                'version {} not support {} models, auto switch to version {}'.
                format(version, model_type, DEFAULT_MODEL_VERSION))
            version = DEFAULT_MODEL_VERSION
        else:
            logger.error('{} models is not support, we only support {}'.format(
-                model_type, MODEL_URLS[DEFAULT_MODEL_VERSION].keys()))
+                model_type, model_urls[DEFAULT_MODEL_VERSION].keys()))
            sys.exit(-1)
-    if lang not in MODEL_URLS[version][model_type]:
+    if lang not in model_urls[version][model_type]:
-        if lang in MODEL_URLS[DEFAULT_MODEL_VERSION][model_type]:
+        if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]:
-            logger.warning('lang {} is not support in {}, use {} instead'.
+            logger.warning(
+                'lang {} is not support in {}, auto switch to version {}'.
                format(lang, version, DEFAULT_MODEL_VERSION))
            version = DEFAULT_MODEL_VERSION
        else:
            logger.error(
                'lang {} is not support, we only support {} for {} models'.
-                format(lang, MODEL_URLS[DEFAULT_MODEL_VERSION][model_type].keys(
+                format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys(
                ), model_type))
            sys.exit(-1)
-    return MODEL_URLS[version][model_type][lang]
+    return model_urls[version][model_type][lang]
 class PaddleOCR(predict_system.TextSystem):
@@ -265,23 +296,28 @@ class PaddleOCR(predict_system.TextSystem):
        """
        params = parse_args(mMain=False)
        params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
        if not params.show_log:
            logger.setLevel(logging.INFO)
        self.use_angle_cls = params.use_angle_cls
        lang, det_lang = parse_lang(params.lang)
        # init model dir
-        det_model_config = get_model_config(params.version, 'det', det_lang)
+        det_model_config = get_model_config('OCR', params.ocr_version, 'det',
+                                            det_lang)
        params.det_model_dir, det_url = confirm_model_dir_url(
            params.det_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
            det_model_config['url'])
-        rec_model_config = get_model_config(params.version, 'rec', lang)
+        rec_model_config = get_model_config('OCR', params.ocr_version, 'rec',
+                                            lang)
        params.rec_model_dir, rec_url = confirm_model_dir_url(
            params.rec_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
            rec_model_config['url'])
-        cls_model_config = get_model_config(params.version, 'cls', 'ch')
+        cls_model_config = get_model_config('OCR', params.ocr_version, 'cls',
+                                            'ch')
        params.cls_model_dir, cls_url = confirm_model_dir_url(
            params.cls_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
@@ -362,22 +398,27 @@ class PPStructure(OCRSystem):
    def __init__(self, **kwargs):
        params = parse_args(mMain=False)
        params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
        if not params.show_log:
            logger.setLevel(logging.INFO)
        lang, det_lang = parse_lang(params.lang)
        # init model dir
-        det_model_config = get_model_config(params.version, 'det', det_lang)
+        det_model_config = get_model_config('OCR', params.ocr_version, 'det',
+                                            det_lang)
        params.det_model_dir, det_url = confirm_model_dir_url(
            params.det_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
            det_model_config['url'])
-        rec_model_config = get_model_config(params.version, 'rec', lang)
+        rec_model_config = get_model_config('OCR', params.ocr_version, 'rec',
+                                            lang)
        params.rec_model_dir, rec_url = confirm_model_dir_url(
            params.rec_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
            rec_model_config['url'])
-        table_model_config = get_model_config(params.version, 'table', 'en')
+        table_model_config = get_model_config(
+            'STRUCTURE', params.structure_version, 'table', 'en')
        params.table_model_dir, table_url = confirm_model_dir_url(
            params.table_model_dir,
            os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),