Merge branch 'dygraph' into lite

3f1cb773 · Double_V · GitHub · af1ac7c2 · f687e092 · 3f1cb773
Unverified Commit 3f1cb773 authored Mar 16, 2021 by Double_V Committed by GitHub Mar 16, 2021
20 changed files
--- a/doc/doc_ch/models_list.md
+++ b/doc/doc_ch/models_list.md
 ## OCR模型列表（V2.0，2021年1月20日更新）
-**说明** ：2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
+> **说明**
+> 1. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
+> 2. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
 - [一、文本检测模型](#文本检测模型)
 - [二、文本识别模型](#文本识别模型)
@@ -12,9 +16,14 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 |模型类型|模型格式|简介|
 |--- | --- | --- |
-|推理模型|inference.pdmodel、inference.pdiparams|用于python预测引擎推理，[详情](./inference.md)|
+|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理，[详情](./inference.md)|
 |训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练|
-|slim模型|\*.nb|用于lite部署|
+|slim模型|\*.nb|经过飞桨模型压缩工具PaddleSlim压缩后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。|
+各个模型的关系如下面的示意图所示。
+![](../imgs/model_prod_flow_ch.png)
 <a name="文本检测模型"></a>

--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
 ## 文字识别
- [一、数据准备](#数据准备)
+- [1 数据准备](#数据准备)
-    - [数据下载](#数据下载)
+    - [1.1 自定义数据集](#自定义数据集)
-    - [自定义数据集](#自定义数据集)  
+    - [1.2 数据下载](#数据下载)
-    - [字典](#字典)  
+    - [1.3 字典](#字典)  
-    - [支持空格](#支持空格)
+    - [1.4 支持空格](#支持空格)
- [二、启动训练](#启动训练)
+- [2 启动训练](#启动训练)
-    - [1. 数据增强](#数据增强)
+    - [2.1 数据增强](#数据增强)
-    - [2. 训练](#训练)
+    - [2.2 训练](#训练)
-    - [3. 小语种](#小语种)
+    - [2.3 小语种](#小语种)
- [三、评估](#评估)
+- [3 评估](#评估)
- [四、预测](#预测)
+- [4 预测](#预测)
-    - [1. 训练引擎预测](#训练引擎预测)
+    - [4.1 训练引擎预测](#训练引擎预测)
 <a name="数据准备"></a>
-### 数据准备
+### 1. 数据准备
-PaddleOCR 支持两种数据格式: `lmdb` 用于训练公开数据，调试算法; `通用数据` 训练自己的数据:
+PaddleOCR 支持两种数据格式:
+ - `lmdb` 用于训练以lmdb格式存储的数据集;
-请按如下步骤设置数据集：
+ - `通用数据` 用于训练以文本文件存储的数据集:
 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：
 ```
+# linux and mac os
 ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
+# windows
+mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>
 ```
-<a name="数据下载"></a>
+<a name="准备数据集"></a>
-* 数据下载
+#### 1.1 自定义数据集
+下面以通用数据集为例， 介绍如何准备数据集：
-若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
-<a name="自定义数据集"></a>
+* 训练集
-* 使用自己数据集
-若您希望使用自己的数据进行训练，请参考下文组织您的数据。
+建议将训练图片放入同一个文件夹，并用一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下:
- 训练集
+**注意：** txt文件中默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错。
-首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（rec_gt_train.txt）记录图片路径和标签。
+```
+" 图像文件名                 图像标注信息 "
-**注意：** 默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错
+train_data/rec/train/word_001.jpg   简单可依赖
+train_data/rec/train/word_002.jpg   用科技让复杂的世界更简单
+...
+```
+最终训练集应有如下文件结构：
 ```
-" 图像文件名                 图像标注信息 "
+|-train_data
+  |-rec
+    |- rec_gt_train.txt
+    |- train
+        |- word_001.png
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+```
+- 测试集
+同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，测试集的结构如下所示：
-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 提供了一份用于训练 icdar2015 数据集的标签文件，通过以下方式下载：
+|-train_data
+  |-rec
+    |- rec_gt_test.txt
+    |- test
+        |- word_001.jpg
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+```
+<a name="数据下载"></a>
+1.2 数据下载
+若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，下载 benchmark 所需的lmdb格式数据集。
+如果你使用的是icdar2015的公开数据集，PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通过以下方式下载：
+如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
 ```
 # 训练集标签
@@ -70,34 +104,8 @@ PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支
 python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
 ```
-最终训练集应有如下文件结构：
-```
-|-train_data
-    |-ic15_data
-        |- rec_gt_train.txt
-        |- train
-            |- word_001.png
-            |- word_002.jpg
-            |- word_003.jpg
-            | ...
-```
- 测试集
-同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，测试集的结构如下所示：
-```
-|-train_data
-    |-ic15_data
-        |- rec_gt_test.txt
-        |- test
-            |- word_001.jpg
-            |- word_002.jpg
-            |- word_003.jpg
-            | ...
-```
 <a name="字典"></a>
- 字典
+1.3 字典
 最后需要提供一个字典（{word_dict_name}.txt），使模型在训练时，可以将所有出现的字符映射为字典的索引。
@@ -114,6 +122,10 @@ n
 word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，“and” 将被映射成 [2 5 1]
+* 内置字典
+PaddleOCR内置了一部分字典，可以按需使用。
 `ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典
 `ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典
@@ -129,10 +141,10 @@ 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/dict/en_dict.txt` 是一个包含63个字符的英文字典
-您可以按需使用。
 目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**，
-如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict) 将语料文件提交至[corpus](../../ppocr/utils/corpus)，我们会在Repo中感谢您。
+如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict)，我们会在Repo中感谢您。
 - 自定义字典
@@ -140,13 +152,13 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 并将 `character_type` 设置为 `ch`。
 <a name="支持空格"></a>
- 添加空格类别
+1.4 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`。
 <a name="启动训练"></a>
-### 启动训练
+### 2. 启动训练
 PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN 识别模型为例：
@@ -171,7 +183,7 @@ tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_icdar15_train.yml
 ```
 <a name="数据增强"></a>
- 数据增强
+#### 2.1 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`。
@@ -182,7 +194,7 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入
 *由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
 <a name="训练"></a>
- 训练
+#### 2.2 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` 。
@@ -195,13 +207,14 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
 | :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   |
 | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
 | [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) |  CRNN | ResNet34_vd |  None   |  BiLSTM |  ctc  |
-| rec_chinese_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
-| rec_chinese_common_train.yml |  CRNN |   ResNet34_vd |  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_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_mv3_tps_bilstm_att.yml |  CRNN |   Mobilenet_v3 |  TPS   |  BiLSTM |  att  |
+| rec_r34_vd_tps_bilstm_att.yml |  CRNN |   Resnet34_vd |  TPS   |  BiLSTM |  att  |
+| rec_r50fpn_vd_none_srn.yml    | SRN | Resnet50_fpn_vd    | None    | rnn | srn |
 训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
@@ -270,18 +283,111 @@ Eval:
 **注意，预测/评估时的配置文件请务必与训练一致。**
 <a name="小语种"></a>
- 小语种
+#### 2.3 小语种
+PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
-PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有：
+您有两种方式创建所需的配置文件：
-| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language |
+1. 通过脚本自动生成
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语   |
+[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) 可以帮助您生成多语言模型的配置文件
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   |
+- 以意大利语为例，如果您的数据是按如下格式准备的：
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  |
+    ```
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  |
+    |-train_data
+        |- it_train.txt # 训练集标签
+        |- it_val.txt # 验证集标签
+        |- data
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+    ```
+    可以使用默认参数，生成配置文件：
+    ```bash
+    # 该代码需要在指定目录运行
+    cd PaddleOCR/configs/rec/multi_language/
+    # 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件
+    python3 generate_multi_language_configs.py -l it
+    ```
+- 如果您的数据放置在其他位置，或希望使用自己的字典，可以通过指定相关参数来生成配置文件:
+    ```bash
+    # -l或者--language字段是必须的
+    # --train修改训练集，--val修改验证集，--data_dir修改数据集目录，--dict修改字典路径， -o修改对应默认参数
+    cd PaddleOCR/configs/rec/multi_language/
+    python3 generate_multi_language_configs.py -l it \  # 语种
+    --train {path/of/train_label.txt} \ # 训练标签文件的路径
+    --val {path/of/val_label.txt} \     # 验证集标签文件的路径
+    --data_dir {train_data/path} \      # 训练数据的根目录
+    --dict {path/of/dict} \             # 字典文件路径
+    -o Global.use_gpu=False             # 是否使用gpu
+    ...
+    ```
+2. 手动修改配置文件
+   您也可以手动修改模版中的以下几个字段:
+   ```
+    Global:
+      use_gpu: True
+      epoch_num: 500
+      ...
+      character_type: it  # 需要识别的语种
+      character_dict_path:  {path/of/dict} # 字典文件所在路径
+   Train:
+      dataset:
+        name: SimpleDataSet
+        data_dir: train_data/ # 数据存放根目录
+        label_file_list: ["./train_data/train_list.txt"] # 训练集label路径
+      ...
+   Eval:
+      dataset:
+        name: SimpleDataSet
+        data_dir: train_data/ # 数据存放根目录
+        label_file_list: ["./train_data/val_list.txt"] # 验证集label路径
+      ...
+   ```
+目前PaddleOCR支持的多语言算法有：
+| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language | character_type |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 中文繁体  | chinese_cht|
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语（区分大小写）   | EN |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  french |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   | german |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  | japan |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  | korean |
+| rec_it_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 意大利语  | it |
+| rec_xi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 西班牙语 |  xi |
+| rec_pu_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 葡萄牙语   | pu |
+| rec_ru_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 俄罗斯语  | ru |
+| rec_ar_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯语  | ar |
+| rec_hi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 印地语 |  hi |
+| rec_ug_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 维吾尔语  | ug |
+| rec_fa_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 波斯语  | fa |
+| rec_ur_ite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 乌尔都语  | ur |
+| rec_rs_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 塞尔维亚(latin)语 | rs |
+| rec_oc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 欧西坦语   | oc |
+| rec_mr_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 马拉地语  | mr |
+| rec_ne_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 尼泊尔语  | ne |
+| rec_rsc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 塞尔维亚(cyrillic)语 |  rsc |
+| rec_bg_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 保加利亚语  | bg |
+| rec_uk_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 乌克兰语  | uk |
+| rec_be_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 白俄罗斯语   | be |
+| rec_te_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 泰卢固语  | te |
+| rec_ka_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 卡纳达语  | ka |
+| rec_ta_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 泰米尔语 |  ta |
 多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
@@ -320,7 +426,7 @@ Eval:
    ...
 ```
 <a name="评估"></a>
-### 评估
+### 3 评估
 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml`  修改Eval中的 `label_file_path` 设置。
@@ -330,10 +436,10 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 ```
 <a name="预测"></a>
-### 预测
+### 4 预测
 <a name="训练引擎预测"></a>
-* 训练引擎的预测
+#### 4.1 训练引擎的预测
 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。

--- a/doc/doc_ch/whl.md
+++ b/doc/doc_ch/whl.md
 # paddleocr package使用说明
-## 快速上手
+## 1 快速上手
-### 安装whl包
+### 1.1 安装whl包
 pip安装
 ```bash
@@ -14,9 +14,12 @@ pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
 python3 setup.py bdist_wheel
 pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
 ```
-### 1. 代码使用
-* 检测+分类+识别全流程
+## 2 使用
+### 2.1 代码使用
+paddleocr whl包会自动下载ppocr轻量级模型作为默认模型，可以根据第3节**自定义模型**进行自定义更换。
+* 检测+方向分类器+识别全流程
 ```python
 from paddleocr import PaddleOCR, draw_ocr
 # Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
@@ -84,7 +87,7 @@ im_show.save('result.jpg')
 </div>
-* 分类+识别
+* 方向分类器+识别
 ```python
 from paddleocr import PaddleOCR
 ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
@@ -143,7 +146,7 @@ for line in result:
 ['韩国小馆', 0.9907421]
 ```
-* 单独执行分类
+* 单独执行方向分类器
 ```python
 from paddleocr import PaddleOCR
 ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
@@ -157,14 +160,14 @@ for line in result:
 ['0', 0.9999924]
 ```
-### 通过命令行使用
+### 2.2 通过命令行使用
 查看帮助信息
 ```bash
 paddleocr -h
 ```
-* 检测+分类+识别全流程
+* 检测+方向分类器+识别全流程
 ```bash
 paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
 ```
@@ -188,7 +191,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
 ......
 ```
-* 分类+识别
+* 方向分类器+识别
 ```bash
 paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false
 ```
@@ -220,7 +223,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
 ['韩国小馆', 0.9907421]
 ```
-* 单独执行分类
+* 单独执行方向分类器
 ```bash
 paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec false
 ```
@@ -230,11 +233,11 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls tru
 ['0', 0.9999924]
 ```
-## 自定义模型
+## 3 自定义模型
 当内置模型无法满足需求时，需要使用到自己训练的模型。
 首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
-### 代码使用
+### 3.1 代码使用
 ```python
 from paddleocr import PaddleOCR, draw_ocr
 # 模型路径下必须含有model和params文件
@@ -255,17 +258,17 @@ im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
-### 通过命令行使用
+### 3.2 通过命令行使用
 ```bash
 paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true
 ```
-### 使用网络图片或者numpy数组作为输入
+## 4 使用网络图片或者numpy数组作为输入
-1. 网络图片
+### 4.1 网络图片
-代码使用
+- 代码使用
 ```python
 from paddleocr import PaddleOCR, draw_ocr
 # Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
@@ -286,12 +289,12 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
-命令行模式
+- 命令行模式
 ```bash
 paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
 ```
-2. numpy数组
+### 4.2 numpy数组
 仅通过代码使用时支持numpy数组作为输入
 ```python
 from paddleocr import PaddleOCR, draw_ocr
@@ -301,7 +304,7 @@ ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to downlo
 img_path = 'PaddleOCR/doc/imgs/11.jpg'
 img = cv2.imread(img_path)
 # img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消
-result = ocr.ocr(img_path, cls=True)
+result = ocr.ocr(img, cls=True)
 for line in result:
    print(line)
@@ -316,7 +319,7 @@ im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
-## 参数说明
+## 5 参数说明
 | 字段                    | 说明                                                                                                                                                                                                                 | 默认值                  |
 |-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|

--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -42,8 +42,8 @@ PaddleOCR open-source text recognition algorithms list:
 - [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
 - [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
 - [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
 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:
@@ -55,5 +55,8 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 |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)|
 |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|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)|
+|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|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)|
+|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)|
 Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
 ## TEXT ANGLE CLASSIFICATION
+### Method introduction
+The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system,
+The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support.
+Example of 0 and 180 degree data samples：
+![](../imgs_results/angle_class_example.jpg)
 ### DATA PREPARATION
 Please organize the dataset as follows:
@@ -66,7 +73,7 @@ Start training:
 ```
 # Set PYTHONPATH path
 export PYTHONPATH=$PYTHONPATH:.
-# GPU training Support single card and multi-card training, specify the card number through --gpus. If your paddle version is less than 2.0rc1, please use '--selected_gpus'
+# GPU training Support single card and multi-card training, specify the card number through --gpus.
 # Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
 python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/cls/cls_mv3.yml
 ```

--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -76,7 +76,7 @@ You can also use `-o` to change the training parameters without modifying the ym
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 # multi-GPU training
-# Set the GPU ID used by the '--gpus' parameter; If your paddle version is less than 2.0rc1, please use '--selected_gpus'
+# 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 Optimizer.base_lr=0.0001

--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -5,7 +5,8 @@ The inference model (the model saved by `paddle.jit.save`) is generally a solidi
 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://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md).
+Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
+For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/docs/zh_CN/extension/paddle_mobile_inference.md).
 Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model.
@@ -25,6 +26,7 @@ Next, we first introduce how to convert a trained model into an inference model,
 - [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
    - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
    - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
+    - [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
    - [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
    - [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
@@ -146,7 +148,7 @@ The visual text detection results are saved to the ./inference_results folder by
 ![](../imgs_results/det_res_00018069.jpg)
 You can use the parameters `limit_type` and `det_limit_side_len` to limit the size of the input image,
-The optional parameters of `litmit_type` are [`max`, `min`], and
+The optional parameters of `limit_type` are [`max`, `min`], and
 `det_limit_size_len` is a positive integer, generally set to a multiple of 32, such as 960.
 The default setting of the parameters is `limit_type='max', det_limit_side_len=960`. Indicates that the longest side of the network input image cannot exceed 960,
@@ -304,8 +306,23 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
+<a name="SRN-BASED_RECOGNITION"></a>
+### 3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
+The recognition model based on SRN requires additional setting of the recognition algorithm parameter
+--rec_algorithm="SRN". At the same time, it is necessary to ensure that the predicted shape is consistent
+with the training, such as: --rec_image_shape="1, 64, 256"
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
+                                    --rec_model_dir="./inference/srn/" \
+                                    --rec_image_shape="1, 64, 256" \
+                                    --rec_char_type="en" \
+                                    --rec_algorithm="SRN"
+```
 <a name="USING_CUSTOM_CHARACTERS"></a>
-### 3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
 If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
 ```
@@ -313,12 +330,12 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 ```
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
-### 4. MULTILINGAUL MODEL INFERENCE
+### 5. 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,
-You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition:
+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:
 ```
-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/korean.ttf"
+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)

--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
@@ -3,7 +3,7 @@
 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 1.8+, Recommend PaddlePaddle 2.0rc1
+- PaddlePaddle 2.0.0
 - python3.7
 - glibc 2.23
@@ -33,15 +33,15 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags
 sudo docker container exec -it ppocr /bin/bash
 ```
-**2. Install PaddlePaddle Fluid v2.0**
+**2. Install PaddlePaddle 2.0**
 ```
 pip3 install --upgrade pip
 # If you have cuda9 or cuda10 installed on your machine, please run the following command to install
-python3 -m pip install paddlepaddle-gpu==2.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
 # If you only have cpu on your machine, please run the following command to install
-python3 -m pip install paddlepaddle==2.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
 ```
 For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.

--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
 ## OCR model list（V2.0, updated on 2021.1.20）
-**Note** : 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.
+> **Note**
+> 1. 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.
+> 2. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
 - [1. Text Detection Model](#Detection)
 - [2. Text Recognition Model](#Recognition)
@@ -12,9 +14,13 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine
 |model type|model format|description|
 |--- | --- | --- |
-|inference model|inference.pdmodel、inference.pdiparams|Used for reasoning based on Python prediction engine，[detail](./inference_en.md)|
+|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)|
 |trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
-|slim model|\*.nb|Generally used for Lite deployment|
+|slim model|\*.nb| Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
+Relationship of the above models is as follows.
+![](../imgs_en/model_prod_flow_en.png)
 <a name="Detection"></a>
 ### 1. Text Detection Model
@@ -80,7 +86,7 @@ If you want to train your own model, you can prepare the training set file, veri
 cd {your/path/}PaddleOCR/configs/rec/multi_language/
 # The -l or --language parameter is required
 # --train modify train_list path
-# --val modify eval_list path 
+# --val modify eval_list path
 # --data_dir modify data dir
 # -o modify default parameters
 # --dict Change the dictionary path. The example uses the default dictionary path, so that this parameter can be empty.
@@ -93,7 +99,7 @@ python3 generate_multi_language_configs.py -l it \
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
 | french_mobile_v2.0_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
-| german_mobile_v2.0_rec |Lightweight model for French recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
+| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
 | korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
 | japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
 | it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |

--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
 ## TEXT RECOGNITION
- [DATA PREPARATION](#DATA_PREPARATION)
+- [1 DATA PREPARATION](#DATA_PREPARATION)
-    - [Dataset Download](#Dataset_download)
+    - [1.1 Costom Dataset](#Costom_Dataset)
-    - [Costom Dataset](#Costom_Dataset)  
+    - [1.2 Dataset Download](#Dataset_download)
-    - [Dictionary](#Dictionary)  
+    - [1.3 Dictionary](#Dictionary)  
-    - [Add Space Category](#Add_space_category)
+    - [1.4 Add Space Category](#Add_space_category)
- [TRAINING](#TRAINING)
+- [2 TRAINING](#TRAINING)
-    - [Data Augmentation](#Data_Augmentation)
+    - [2.1 Data Augmentation](#Data_Augmentation)
-    - [Training](#Training)
+    - [2.2 Training](#Training)
-    - [Multi-language](#Multi_language)
+    - [2.3 Multi-language](#Multi_language)
- [EVALUATION](#EVALUATION)
+- [3 EVALUATION](#EVALUATION)
- [PREDICTION](#PREDICTION)
+- [4 PREDICTION](#PREDICTION)
-    - [Training engine prediction](#Training_engine_prediction)
+    - [4.1 Training engine prediction](#Training_engine_prediction)
 <a name="DATA_PREPARATION"></a>
 ### 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:
+PaddleOCR supports two data formats:
+- `LMDB` is used to train data sets stored in lmdb format;
+- `general data` is used to train data sets stored in text files:
 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:
 ```
+# linux and mac os
 ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
+# windows
+mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>
 ```
-<a name="Dataset_download"></a>
-* 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
-If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
 <a name="Costom_Dataset"></a>
-* Use your own dataset:
+#### 1.1 Costom 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.
+It is recommended to put the training images in the same folder, and use a txt file (rec_gt_train.txt) to store the image path and label. The contents of the txt file are as follows:
 * 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/rec/train/word_001.jpg   简单可依赖
-train_data/train_0002.jpg   用科技让复杂的世界更简单
+train_data/rec/train/word_002.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
-        |- rec_gt_train.txt
+    |- rec_gt_train.txt
-        |- train
+    |- train
-            |- word_001.png
+        |- word_001.png
-            |- word_002.jpg
+        |- word_002.jpg
-            |- word_003.jpg
+        |- word_003.jpg
-            | ...
+        | ...
 ```
 - Test set
@@ -82,6 +73,7 @@ Similar to the training set, the test set also needs to be provided a folder con
 ```
 |-train_data
+  |-rec
    |-ic15_data
        |- rec_gt_test.txt
        |- test
@@ -90,8 +82,25 @@ Similar to the training set, the test set also needs to be provided a folder con
            |- word_003.jpg
            | ...
 ```
+<a name="Dataset_download"></a>
+#### 1.2 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
+If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
+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
+```
 <a name="Dictionary"></a>
- Dictionary
+#### 1.3 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.
@@ -108,6 +117,8 @@ 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]
+PaddleOCR has built-in dictionaries, which can be used on demand.
 `ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.
 `ppocr/utils/ic15_dict.txt` is an English dictionary with 63 characters
@@ -123,10 +134,8 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
 `ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
-You can use it on demand.
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
-If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) or corpus file to [corpus](../../ppocr/utils/corpus) and we will thank you in the Repo.
+If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
 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`.
@@ -136,14 +145,14 @@ To customize the dict file, please modify the `character_dict_path` field in `co
 If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.
 <a name="Add_space_category"></a>
- Add space category
+#### 1.4 Add space category
 If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
 **Note: use_space_char only takes effect when character_type=ch**
 <a name="TRAINING"></a>
-### TRAINING
+### 2 TRAINING
 PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
@@ -166,7 +175,7 @@ Start training:
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_icdar15_train.yml
 ```
 <a name="Data_Augmentation"></a>
- Data Augmentation
+#### 2.1 Data Augmentation
 PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
@@ -175,7 +184,7 @@ The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, rand
 Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
 <a name="Training"></a>
- Training
+#### 2.2 Training
 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.
@@ -195,6 +204,10 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
 | rec_mv3_none_none_ctc.yml |  Rosetta |   Mobilenet_v3 large 0.5 |  None   |  None |  ctc  |
 | 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_mv3_tps_bilstm_att.yml |  CRNN |   Mobilenet_v3 |  TPS   |  BiLSTM |  att  |
+| rec_r34_vd_tps_bilstm_att.yml |  CRNN |   Resnet34_vd |  TPS   |  BiLSTM |  att  |
+| rec_r50fpn_vd_none_srn.yml    | SRN | Resnet50_fpn_vd    | None    | rnn | srn |
 For training Chinese data, it is recommended to use
 [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.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:
@@ -264,17 +277,118 @@ Eval:
 **Note that the configuration file for prediction/evaluation must be consistent with the training.**
 <a name="Multi_language"></a>
- Multi-language
+#### 2.3 Multi-language
+PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is
+provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
+There are two ways to create the required configuration file:：
+1. Automatically generated by script
+[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
+- Take Italian as an example, if your data is prepared in the following format:
+    ```
+    |-train_data
+        |- it_train.txt # train_set label
+        |- it_val.txt # val_set label
+        |- data
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+    ```
+    You can use the default parameters to generate a configuration file:
+    ```bash
+    # The code needs to be run in the specified directory
+    cd PaddleOCR/configs/rec/multi_language/
+    # Set the configuration file of the language to be generated through the -l or --language parameter.
+    # This command will write the default parameters into the configuration file
+    python3 generate_multi_language_configs.py -l it
+    ```
+- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:
+    ```bash
+    # -l or --language field is required
+    # --train to modify the training set
+    # --val to modify the validation set
+    # --data_dir to modify the data set directory
+    # --dict to modify the dict path
+    # -o to modify the corresponding default parameters
+    cd PaddleOCR/configs/rec/multi_language/
+    python3 generate_multi_language_configs.py -l it \  # language
+    --train {path/of/train_label.txt} \ # path of train_label
+    --val {path/of/val_label.txt} \     # path of val_label
+    --data_dir {train_data/path} \      # root directory of training data
+    --dict {path/of/dict} \             # path of dict
+    -o Global.use_gpu=False             # whether to use gpu
+    ...
+    ```
+2. Manually modify the configuration file
+   You can also manually modify the following fields in the template:
+   ```
+    Global:
+      use_gpu: True
+      epoch_num: 500
+      ...
+      character_type: it  # language
+      character_dict_path:  {path/of/dict} # path of dict
+   Train:
+      dataset:
+        name: SimpleDataSet
+        data_dir: train_data/ # root directory of training data
+        label_file_list: ["./train_data/train_list.txt"] # train label path
+      ...
+   Eval:
+      dataset:
+        name: SimpleDataSet
+        data_dir: train_data/ # root directory of val data
+        label_file_list: ["./train_data/val_list.txt"] # val label path
+      ...
-PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are:
+   ```
+Currently, the multi-language algorithms supported by PaddleOCR are:
+| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
+| rec_it_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Italian  | it |
+| rec_xi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Spanish |  xi |
+| rec_pu_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Portuguese   | pu |
+| rec_ru_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Russia  | ru |
+| rec_ar_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Arabic  | ar |
+| rec_hi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Hindi |  hi |
+| rec_ug_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Uyghur  | ug |
+| rec_fa_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Persian(Farsi)  | fa |
+| rec_ur_ite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Urdu  | ur |
+| rec_rs_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(latin) | rs |
+| rec_oc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Occitan  | oc |
+| rec_mr_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Marathi  | mr |
+| rec_ne_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Nepali  | ne |
+| rec_rsc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(cyrillic) |  rsc |
+| rec_bg_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Bulgarian  | bg |
+| rec_uk_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Ukranian  | uk |
+| rec_be_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Belarusian   | be |
+| rec_te_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Telugu  | te |
+| rec_ka_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Kannada  | ka |
+| rec_ta_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Tamil |  ta |
-| Configuration file | Algorithm name | backbone | trans | seq | pred | language ｜
-| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
-| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English ｜
-| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French ｜
-| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German ｜
-| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese ｜
-| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean ｜
 The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
@@ -315,7 +429,7 @@ Eval:
 ```
 <a name="EVALUATION"></a>
-### EVALUATION
+### 3 EVALUATION
 The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
@@ -325,10 +439,10 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 ```
 <a name="PREDICTION"></a>
-### PREDICTION
+### 4 PREDICTION
 <a name="Training_engine_prediction"></a>
-* Training engine prediction
+#### 4.1 Training engine prediction
 Using the model trained by paddleocr, you can quickly get prediction through the following script.

--- a/doc/doc_en/whl_en.md
+++ b/doc/doc_en/whl_en.md
 # paddleocr package
-## Get started quickly
+## 1 Get started quickly
-### install package
+### 1.1 install package
 install by pypi
 ```bash
 pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
@@ -12,9 +12,11 @@ build own whl package and install
 python3 setup.py bdist_wheel
 pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version of paddleocr
 ```
-### 1. Use by code
+## 2 Use
+### 2.1 Use by code
+The paddleocr whl package will automatically download the ppocr lightweight model as the default model, which can be customized and replaced according to the section 3 **Custom Model**.
-* detection classification and recognition
+* detection angle classification and recognition
 ```python
 from paddleocr import PaddleOCR,draw_ocr
 # Paddleocr supports Chinese, English, French, German, Korean and Japanese.
@@ -163,7 +165,7 @@ Output will be a list, each item contains classification result and confidence
 ['0', 0.99999964]
 ```
-### Use by command line
+### 2.2 Use by command line
 show help information
 ```bash
@@ -239,11 +241,11 @@ Output will be a list, each item contains classification result and confidence
 ['0', 0.99999964]
 ```
-## Use custom model
+## 3 Use custom model
 When the built-in model cannot meet the needs, you need to use your own trained model.
 First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
-### 1. Use by code
+### 3.1 Use by code
 ```python
 from paddleocr import PaddleOCR,draw_ocr
@@ -265,17 +267,17 @@ im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
-### Use by command line
+### 3.2 Use by command line
 ```bash
 paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true
 ```
-### Use web images or numpy array as input
+## 4 Use web images or numpy array as input
-1. Web image
+### 4.1 Web image
-Use by code
+- Use by code
 ```python
 from paddleocr import PaddleOCR, draw_ocr
 ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
@@ -294,12 +296,12 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
-Use by command line
+- Use by command line
 ```bash
 paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
 ```
-2. Numpy array
+### 4.2 Numpy array
 Support numpy array as input only when used by code
 ```python
@@ -324,7 +326,7 @@ im_show.save('result.jpg')
 ```
-## Parameter Description
+## 5 Parameter Description
 | Parameter                    | Description                                                                                                                                                                                                                 | Default value                  |
 |-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|

--- a/doc/fonts/arabic.ttf
+++ b/doc/fonts/arabic.ttf
--- a/doc/fonts/chinese_cht.ttf
+++ b/doc/fonts/chinese_cht.ttf
--- a/doc/fonts/cyrillic.ttf
+++ b/doc/fonts/cyrillic.ttf
--- a/doc/french.ttf
+++ b/doc/french.ttf
--- a/doc/german.ttf
+++ b/doc/german.ttf
--- a/doc/fonts/hindi.ttf
+++ b/doc/fonts/hindi.ttf
--- a/doc/japan.ttc
+++ b/doc/japan.ttc
--- a/doc/fonts/kannada.ttf
+++ b/doc/fonts/kannada.ttf
--- a/doc/korean.ttf
+++ b/doc/korean.ttf