Merge remote-tracking branch 'origin/dygraph' into dygraph

configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/v3_ta_mobile
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/dict/ta_dict.txt
+  max_text_length: &max_text_length 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_ppocrv3_ta.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 3.0e-05
+
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+    last_conv_stride: [1, 2]
+    last_pool_type: avg
+  Head:
+    name: MultiHead
+    head_list:
+      - CTCHead:
+          Neck:
+            name: svtr
+            dims: 64
+            depth: 2
+            hidden_dims: 120
+            use_guide: True
+          Head:
+            fc_decay: 0.00001
+      - SARHead:
+          enc_dim: 512
+          max_text_length: *max_text_length
+
+Loss:
+  name: MultiLoss
+  loss_config_list:
+    - CTCLoss:
+    - SARLoss:
+
+PostProcess:  
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  ignore_space: False
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    ext_op_transform_idx: 1
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecConAug:
+        prob: 0.5
+        ext_data_num: 2
+        image_shape: [48, 320, 3]
+    - RecAug:
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

configs/rec/PP-OCRv3/multi_language/te_PP-OCRv3_rec.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/v3_te_mobile
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/dict/te_dict.txt
+  max_text_length: &max_text_length 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_ppocrv3_te.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 3.0e-05
+
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+    last_conv_stride: [1, 2]
+    last_pool_type: avg
+  Head:
+    name: MultiHead
+    head_list:
+      - CTCHead:
+          Neck:
+            name: svtr
+            dims: 64
+            depth: 2
+            hidden_dims: 120
+            use_guide: True
+          Head:
+            fc_decay: 0.00001
+      - SARHead:
+          enc_dim: 512
+          max_text_length: *max_text_length
+
+Loss:
+  name: MultiLoss
+  loss_config_list:
+    - CTCLoss:
+    - SARLoss:
+
+PostProcess:  
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  ignore_space: False
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    ext_op_transform_idx: 1
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecConAug:
+        prob: 0.5
+        ext_data_num: 2
+        image_shape: [48, 320, 3]
+    - RecAug:
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

deploy/Jetson/readme.md

+English | [简体中文](readme_ch.md)
+
+# Jetson Deployment for PaddleOCR
+
+This section introduces the deployment of PaddleOCR on Jetson NX, TX2, nano, AGX and other series of hardware.
+
+
+## 1. Prepare Environment
+
+You need to prepare a Jetson development hardware. If you need TensorRT, you need to prepare the TensorRT environment. It is recommended to use TensorRT version 7.1.3;
+
+1. Install PaddlePaddle in Jetson
+
+The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.
+
+Install PaddlePaddle：
+```shell
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
+```
+
+
+2. Download PaddleOCR code and install dependencies
+
+Clone the PaddleOCR code:
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+and install dependencies：
+```
+cd PaddleOCR
+pip3 install -r requirements.txt
+```
+
+*Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently*
+
+## 2. Perform prediction
+
+Obtain the PPOCR model from the [document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/ppocr_introduction_en.md#6-model-zoo) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:
+
+Download and unzip the PP-OCRv3 models.
+```
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xf ch_PP-OCRv3_det_infer.tar
+tar xf ch_PP-OCRv3_rec_infer.tar
+```
+
+The text detection inference:
+```
+cd PaddleOCR
+python3 tools/infer/predict_det.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/  --image_dir=./doc/imgs/french_0.jpg  --use_gpu=True
+```
+
+After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the `./inference_results/` directory.
+![](./images/det_res_french_0.jpg)
+
+
+The text recognition inference:
+```
+python3 tools/infer/predict_det.py --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/  --image_dir=./doc/imgs_words/en/word_2.png  --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+After executing the command, the predicted information will be printed on the terminal, and the output is as follows:
+```
+[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)
+```
+
+The text  detection and text recognition inference:
+
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/00057937.jpg --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the `./inference_results/` directory.
+![](./images/00057937.jpg)
+
+To enable TRT prediction, you only need to set `--use_tensorrt=True` on the basis of the above command:
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/  --rec_image_shape="3,48,320" --use_gpu=True --use_tensorrt=True
+```
+
+For more ppocr model predictions, please refer to[document](../../doc/doc_en/models_list_en.md)

deploy/Jetson/readme_ch.md

+[English](readme.md) | 简体中文
+
+# Jetson部署PaddleOCR模型
+
+本节介绍PaddleOCR在Jetson NX、TX2、nano、AGX等系列硬件的部署。
+
+
+## 1. 环境准备
+
+需要准备一台Jetson开发板，如果需要TensorRT预测，需准备好TensorRT环境，建议使用7.1.3版本的TensorRT；
+
+1. Jetson安装PaddlePaddle
+
+PaddlePaddle下载[链接](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+请选择适合的您Jetpack版本、cuda版本、trt版本的安装包。
+
+安装命令：
+```shell
+# 安装paddle，以paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl 为例
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
+```
+
+
+2. 下载PaddleOCR代码并安装依赖
+
+首先 clone PaddleOCR 代码：
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+然后，安装依赖：
+```
+cd PaddleOCR
+pip3 install -r requirements.txt
+```
+
+*注：jetson硬件CPU较差，依赖安装较慢，请耐心等待*
+
+
+## 2. 执行预测
+
+从[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/ppocr_introduction.md#6-%E6%A8%A1%E5%9E%8B%E5%BA%93) 模型库中获取PPOCR模型，下面以PP-OCRv3模型为例，介绍在PPOCR模型在jetson上的使用方式：
+
+下载并解压PP-OCRv3模型
+```
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xf ch_PP-OCRv3_det_infer.tar
+tar xf ch_PP-OCRv3_rec_infer.tar
+```
+
+执行文本检测预测：
+```
+cd PaddleOCR
+python3 tools/infer/predict_det.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/  --image_dir=./doc/imgs/french_0.jpg  --use_gpu=True
+```
+
+执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。
+![](./images/det_res_french_0.jpg)
+
+
+执行文本识别预测：
+```
+python3 tools/infer/predict_det.py --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/  --image_dir=./doc/imgs_words/en/word_2.png  --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+执行命令后在终端会打印出预测的信息，输出如下：
+```
+[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)
+```
+
+执行文本检测+文本识别串联预测：
+
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/ --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。
+![](./images/00057937.jpg)
+
+开启TRT预测只需要在以上命令基础上设置`--use_tensorrt=True`即可：
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/00057937.jpg --use_gpu=True --use_tensorrt=True --rec_image_shape="3,48,320"
+```
+
+更多ppocr模型预测请参考[文档](../../doc/doc_ch/models_list.md)

deploy/README.md

@@ -23,10 +23,9 @@ PP-OCR has supported muti deployment schemes. Click the link to get the specific
 - [Python Inference](../doc/doc_en/inference_ppocr_en.md)
 - [C++ Inference](./cpp_infer/readme.md)
 - [Serving (Python/C++)](./pdserving/README.md)
-- [Paddle-Lite (ARM CPU/OpenCL ARM GPU/Metal ARM GPU)](./lite/readme.md)
+- [Paddle-Lite (ARM CPU/OpenCL ARM GPU)](./lite/readme.md)
 - [Paddle.js](./paddlejs/README.md)
-- [Jetson Inference]()
-- [XPU Inference]()
+- [Jetson Inference](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/Jetson/readme.md)
 - [Paddle2ONNX](./paddle2onnx/readme.md)
 
-If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
+If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
\ No newline at end of file

deploy/README_ch.md

@@ -23,10 +23,9 @@ PP-OCR模型已打通多种场景部署方案，点击链接获取具体的使
 - [Python 推理](../doc/doc_ch/inference_ppocr.md)
 - [C++ 推理](./cpp_infer/readme_ch.md)
 - [Serving 服务化部署（Python/C++）](./pdserving/README_CN.md)
-- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU/Metal ARM GPU）](./lite/readme_ch.md)
+- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU）](./lite/readme_ch.md)
 - [Paddle.js 部署](./paddlejs/README_ch.md)
-- [Jetson 推理]()
-- [XPU 推理]()
+- [Jetson 推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/Jetson/readme_ch.md)
 - [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
 
 需要PP-OCR以外的学术算法模型的推理部署，请直接进入相应算法主页面，[入口](../doc/doc_ch/algorithm_overview.md)。
\ No newline at end of file

deploy/cpp_infer/readme.md

@@ -104,7 +104,7 @@ opencv3/
 tar -xf paddle_inference.tgz
 ```
 
-Finally you will see the the folder of `paddle_inference/` in the current path.
+Finally you will see the folder of `paddle_inference/` in the current path.
 
 #### 1.3.2 Compile the inference source code
 * If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
@@ -208,6 +208,8 @@ Execute the built executable file:
 ./build/ppocr [--param1] [--param2] [...]
 ```
 
+**Note**:ppocr uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3, 48, 320`, so if you use the recognition function, you need to add the parameter `--rec_img_h=48`, if you do not use the default `PP-OCRv3` model, you do not need to set this parameter.
+
 Specifically,
 
 ##### 1. det+cls+rec：
@@ -220,6 +222,7 @@ Specifically,
     --det=true \
     --rec=true \
     --cls=true \
+    --rec_img_h=48\
 ```
 
 ##### 2. det+rec：
@@ -231,6 +234,7 @@ Specifically,
     --det=true \
     --rec=true \
     --cls=false \
+    --rec_img_h=48\
 ```
 
 ##### 3. det
@@ -250,6 +254,7 @@ Specifically,
     --det=false \
     --rec=true \
     --cls=true \
+    --rec_img_h=48\
 ```
 
 ##### 5. rec
@@ -260,6 +265,7 @@ Specifically,
     --det=false \
     --rec=true \
     --cls=false \
+    --rec_img_h=48\
 ```
 
 ##### 6. cls
@@ -335,10 +341,10 @@ The detection results will be shown on the screen, which is as follows.
 ```bash
 predict img: ../../doc/imgs/12.jpg
 ../../doc/imgs/12.jpg
-0       det boxes: [[79,553],[399,541],[400,573],[80,585]] rec text: 打浦路252935号 rec score: 0.933757
-1       det boxes: [[31,509],[510,488],[511,529],[33,549]] rec text: 绿洲仕格维花园公寓 rec score: 0.951745
-2       det boxes: [[181,456],[395,448],[396,480],[182,488]] rec text: 打浦路15号 rec score: 0.91956
-3       det boxes: [[43,413],[480,391],[481,428],[45,450]] rec text: 上海斯格威铂尔多大酒店 rec score: 0.915914
+0       det boxes: [[74,553],[427,542],[428,571],[75,582]] rec text: 打浦路252935号 rec score: 0.947724
+1       det boxes: [[23,507],[513,488],[515,529],[24,548]] rec text: 绿洲仕格维花园公寓 rec score: 0.993728
+2       det boxes: [[187,456],[399,448],[400,480],[188,488]] rec text: 打浦路15号 rec score: 0.964994
+3       det boxes: [[42,413],[483,391],[484,428],[43,450]] rec text: 上海斯格威铂尔大酒店 rec score: 0.980086
 The detection visualized image saved in ./output//12.jpg
 ```
 

deploy/cpp_infer/readme_ch.md

@@ -12,7 +12,7 @@
     - [2.3 运行demo](#23)
 - [3. FAQ](#3)
 
-本章节介绍PaddleOCR 模型的的C++部署方法。C++在性能计算上优于Python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成PaddleOCR模型部署。
+本章节介绍PaddleOCR 模型的C++部署方法。C++在性能计算上优于Python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成PaddleOCR模型部署。
 
 
 <a name="1"></a>
@@ -213,6 +213,9 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 
 本demo支持系统串联调用，也支持单个功能的调用，如，只使用检测或识别功能。
 
+**注意** ppocr默认使用`PP-OCRv3`模型，识别模型使用的输入shape为`3,48,320`, 因此如果使用识别功能，需要添加参数`--rec_img_h=48`，如果不使用默认的`PP-OCRv3`模型，则无需设置该参数。
+
+
 运行方式：  
 ```shell
 ./build/ppocr [--param1] [--param2] [...]
@@ -229,6 +232,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=true \
     --rec=true \
     --cls=true \
+    --rec_img_h=48\
 ```
 
 ##### 2. 检测+识别：
@@ -240,6 +244,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=true \
     --rec=true \
     --cls=false \
+    --rec_img_h=48\
 ```
 
 ##### 3. 检测：
@@ -259,6 +264,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=false \
     --rec=true \
     --cls=true \
+    --rec_img_h=48\
 ```
 
 ##### 5. 识别：
@@ -269,6 +275,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --det=false \
     --rec=true \
     --cls=false \
+    --rec_img_h=48\
 ```
 
 ##### 6. 分类：
@@ -343,10 +350,10 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 ```bash
 predict img: ../../doc/imgs/12.jpg
 ../../doc/imgs/12.jpg
-0       det boxes: [[79,553],[399,541],[400,573],[80,585]] rec text: 打浦路252935号 rec score: 0.933757
-1       det boxes: [[31,509],[510,488],[511,529],[33,549]] rec text: 绿洲仕格维花园公寓 rec score: 0.951745
-2       det boxes: [[181,456],[395,448],[396,480],[182,488]] rec text: 打浦路15号 rec score: 0.91956
-3       det boxes: [[43,413],[480,391],[481,428],[45,450]] rec text: 上海斯格威铂尔多大酒店 rec score: 0.915914
+0       det boxes: [[74,553],[427,542],[428,571],[75,582]] rec text: 打浦路252935号 rec score: 0.947724
+1       det boxes: [[23,507],[513,488],[515,529],[24,548]] rec text: 绿洲仕格维花园公寓 rec score: 0.993728
+2       det boxes: [[187,456],[399,448],[400,480],[188,488]] rec text: 打浦路15号 rec score: 0.964994
+3       det boxes: [[42,413],[483,391],[484,428],[43,450]] rec text: 上海斯格威铂尔大酒店 rec score: 0.980086
 The detection visualized image saved in ./output//12.jpg
 ```
 

deploy/hubserving/ocr_det/params.py

@@ -26,7 +26,7 @@ def read_params():
 
     #params for text detector
     cfg.det_algorithm = "DB"
-    cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/"
+    cfg.det_model_dir = "./inference/ch_PP-OCRv3_det_infer/"
     cfg.det_limit_side_len = 960
     cfg.det_limit_type = 'max'
 

deploy/hubserving/ocr_rec/params.py

@@ -26,9 +26,9 @@ def read_params():
 
     #params for text recognizer
     cfg.rec_algorithm = "CRNN"
-    cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/"
+    cfg.rec_model_dir = "./inference/ch_PP-OCRv3_rec_infer/"
 
-    cfg.rec_image_shape = "3, 32, 320"
+    cfg.rec_image_shape = "3, 48, 320"
     cfg.rec_batch_num = 6
     cfg.max_text_length = 25
 

deploy/hubserving/ocr_system/params.py

@@ -26,7 +26,7 @@ def read_params():
 
     #params for text detector
     cfg.det_algorithm = "DB"
-    cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/"
+    cfg.det_model_dir = "./inference/ch_PP-OCRv3_det_infer/"
     cfg.det_limit_side_len = 960
     cfg.det_limit_type = 'max'
 
@@ -44,9 +44,9 @@ def read_params():
 
     #params for text recognizer
     cfg.rec_algorithm = "CRNN"
-    cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/"
+    cfg.rec_model_dir = "./inference/ch_PP-OCRv3_rec_infer/"
 
-    cfg.rec_image_shape = "3, 32, 320"
+    cfg.rec_image_shape = "3, 48, 320"
     cfg.rec_batch_num = 6
     cfg.max_text_length = 25
 

deploy/hubserving/readme.md

@@ -41,6 +41,7 @@ deploy/hubserving/ocr_system/
 ```
 ## 1. 近期更新
 
+* 2022.05.05 新增PP-OCRv3检测和识别模型。
 * 2022.03.30 新增PP-Structure和表格识别两种服务。
 
 ## 2. 快速启动服务
@@ -53,10 +54,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://mirror.baidu.com/pypi/simple
 ```
 
 ### 2.2 下载推理模型
-安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是PP-OCRv2模型，默认模型路径为：
+安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是PP-OCRv3模型，默认模型路径为：
 ```
-检测模型：./inference/ch_PP-OCRv2_det_infer/
-识别模型：./inference/ch_PP-OCRv2_rec_infer/
+检测模型：./inference/ch_PP-OCRv3_det_infer/
+识别模型：./inference/ch_PP-OCRv3_rec_infer/
 方向分类器：./inference/ch_ppocr_mobile_v2.0_cls_infer/
 表格结构识别模型：./inference/en_ppocr_mobile_v2.0_table_structure_infer/
 ```  
@@ -223,6 +224,7 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
 
 - 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。  
 例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，如果需要关闭文本方向分类器，则将参数`use_angle_cls`置为`False`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 **强烈建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。**
+**注意** PPOCR-v3识别模型使用的图片输入shape为`3,48,320`,因此需要修改`params.py`中的`cfg.rec_image_shape = "3, 48, 320"`，如果不使用PPOCR-v3识别模型，则无需修改该参数。
 
 - 3、 卸载旧服务包  
 ```hub uninstall ocr_system```  

deploy/hubserving/readme_en.md

@@ -41,6 +41,7 @@ deploy/hubserving/ocr_system/
 ```
 ## 1. Update
 
+* 2022.05.05 add PP-OCRv3 text detection and recognition models.
 * 2022.03.30 add PP-Structure and table recognition services。
 
 
@@ -55,10 +56,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim
 ```
 
 ### 2.2 Download inference model
-Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv2 models are used, and the default model path is:  
+Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv3 models are used, and the default model path is:  
 ```
-text detection model: ./inference/ch_PP-OCRv2_det_infer/
-text recognition model: ./inference/ch_PP-OCRv2_rec_infer/
+text detection model: ./inference/ch_PP-OCRv3_det_infer/
+text recognition model: ./inference/ch_PP-OCRv3_rec_infer/
 text angle classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/
 tanle recognition: ./inference/en_ppocr_mobile_v2.0_table_structure_infer/
 ```  
@@ -233,6 +234,7 @@ hub serving stop --port/-p XXXX
 ```
 - 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs.  
 For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. If you want to turn off the text direction classifier, set the parameter `use_angle_cls` to `False`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test.  
+**Note** The image input shape used by the PPOCR-v3 recognition model is `3, 48, 320`, so you need to modify `cfg.rec_image_shape = "3, 48, 320"` in `params.py`, if you do not use the PPOCR-v3 recognition model, then there is no need to modify this parameter.
 - 3. Uninstall old service module
 ```shell
 hub uninstall ocr_system

deploy/lite/readme.md

-- [Tutorial of PaddleOCR Mobile deployment](#tutorial-of-paddleocr-mobile-deployment)
-  - [1. Preparation](#1-preparation)
+# Mobile deployment based on Paddle-Lite
+
+- [1. Preparation](#1-preparation)
     - [Preparation environment](#preparation-environment)
     - [1.1 Prepare the cross-compilation environment](#11-prepare-the-cross-compilation-environment)
     - [1.2 Prepare Paddle-Lite library](#12-prepare-paddle-lite-library)
-  - [2 Run](#2-run)
+- [2. Run](#2-run)
     - [2.1 Inference Model Optimization](#21-inference-model-optimization)
     - [2.2 Run optimized model on Phone](#22-run-optimized-model-on-phone)
-      - [注意：](#注意)
-  - [FAQ](#faq)
+- [FAQ](#faq)
 
-# Tutorial of PaddleOCR Mobile deployment
 
-This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
+This tutorial will introduce how to use [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
 
-paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
+Paddle-Lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
 
 ## 1. Preparation
 
@@ -223,7 +222,7 @@ demo/cxx/ocr/
 |-- ocr_db_crnn.cc              C++ main code
 ```
 
-#### 注意：
+**Note**:
 1. `ppocr_keys_v1.txt` is a Chinese dictionary file. If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language. PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
 ```
 dict/french_dict.txt     # french

deploy/lite/readme_ch.md

-- [端侧部署](#端侧部署)
-  - [1. 准备环境](#1-准备环境)
+# 端侧部署
+
+- [1. 准备环境](#1-准备环境)
     - [运行准备](#运行准备)
     - [1.1 准备交叉编译环境](#11-准备交叉编译环境)
     - [1.2 准备预测库](#12-准备预测库)
-  - [2 开始运行](#2-开始运行)
+- [2 开始运行](#2-开始运行)
     - [2.1 模型优化](#21-模型优化)
     - [2.2 与手机联调](#22-与手机联调)
-      - [注意：](#注意)
-  - [FAQ](#faq)
+- [FAQ](#faq)
   
-# 端侧部署
 
 本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleOCR超轻量中文检测、识别模型的详细步骤。
 

deploy/paddle2onnx/readme_ch.md

@@ -39,14 +39,14 @@ python3.7 -m pip install onnxruntime==1.9.0
 有两种方式获取Paddle静态图模型：在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型；
 参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
 
-以 ppocr 中文检测、识别、分类模型为例：
+以 PP-OCRv3 中文检测、识别、分类模型为例：
 
 ```
-wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
-cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && cd ..
 
-wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
-cd ./inference && tar xf ch_PP-OCRv2_rec_infer.tar && cd ..
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+cd ./inference && tar xf ch_PP-OCRv3_rec_infer.tar && cd ..
 
 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
 cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
@@ -57,7 +57,7 @@ cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
 使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式：
 
 ```
-paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
+paddle2onnx --model_dir ./inference/ch_PP-OCRv3_det_infer \
 --model_filename inference.pdmodel \
 --params_filename inference.pdiparams \
 --save_file ./inference/det_onnx/model.onnx \
@@ -65,7 +65,7 @@ paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
 --input_shape_dict="{'x':[-1,3,-1,-1]}" \
 --enable_onnx_checker True
 
-paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
+paddle2onnx --model_dir ./inference/ch_PP-OCRv3_rec_infer \
 --model_filename inference.pdmodel \
 --params_filename inference.pdiparams \
 --save_file ./inference/rec_onnx/model.onnx \
@@ -105,8 +105,8 @@ python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
 ```
 python3.7 tools/infer/predict_system.py --use_gpu=False \
 --cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
---rec_model_dir=./inference/ch_PP-OCRv2_rec_infer \
---det_model_dir=./inference/ch_PP-OCRv2_det_infer \
+--rec_model_dir=./inference/ch_PP-OCRv3_rec_infer \
+--det_model_dir=./inference/ch_PP-OCRv3_det_infer \
 --image_dir=./deploy/lite/imgs/lite_demo.png
 ```
 

deploy/pdserving/README.md

@@ -15,6 +15,14 @@ Some Key Features of Paddle Serving:
 - Industrial serving features supported, such as models management, online loading, online A/B testing etc.
 - Highly concurrent and efficient communication between clients and servers supported.
 
+PaddleServing supports deployment in multiple languages. In this example, two deployment methods, python pipeline and C++, are provided. The comparison between the two is as follows:
+
+| Language | Speed | Secondary development | Do you need to compile |
+|-----|-----|---------|------------|
+| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled |
+| python | general | easy | single-model/multi-model no compilation required |
+
+
 The introduction and tutorial of Paddle Serving service deployment framework reference [document](https://github.com/PaddlePaddle/Serving/blob/develop/README.md).
 
 
@@ -25,6 +33,7 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
   - [Environmental preparation](#environmental-preparation)
   - [Model conversion](#model-conversion)
   - [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
+  - [Paddle Serving C++ deployment](#C++)
   - [WINDOWS Users](#windows-users)
   - [FAQ](#faq)
 
@@ -41,23 +50,23 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
 
 ```bash
 # Install serving which used to start the service
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
-pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
+pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
 
 # Install paddle-serving-server for cuda10.1
-# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
-# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
+# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
+# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
 
 # Install serving which used to start the service
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
-pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
+pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
 
 # Install serving-app
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
-pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
+pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
 ```
 
-   **note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md).
+   **note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md).
 
 
 <a name="model-conversion"></a>
@@ -67,37 +76,37 @@ When using PaddleServing for service deployment, you need to convert the saved i
 Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/README_ch.md#pp-ocr%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8%E6%9B%B4%E6%96%B0%E4%B8%AD) of PPOCR
 ```
 # Download and unzip the OCR text detection model
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
 # Download and unzip the OCR text recognition model
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar &&  tar -xf ch_PP-OCRv2_rec_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar &&  tar -xf ch_PP-OCRv3_rec_infer.tar
 ```
 Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
 ```
 #  Detection model conversion
-python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
+python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
-                                         --serving_client ./ppocr_det_mobile_2.0_client/
+                                         --serving_server ./ppocr_det_v3_serving/ \
+                                         --serving_client ./ppocr_det_v3_client/
 
 #  Recognition model conversion
-python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
+python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
-                                         --serving_client ./ppocr_rec_mobile_2.0_client/
+                                         --serving_server ./ppocr_rec_v3_serving/  \
+                                         --serving_client ./ppocr_rec_v3_client/
 
 ```
 
-After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
+After the detection model is converted, there will be additional folders of `ppocr_det_v3_serving` and `ppocr_det_v3_client` in the current folder, with the following format:
 ```
-|- ppocr_det_mobile_2.0_serving/
+|- ppocr_det_v3_serving/
   |- __model__  
   |- __params__
   |- serving_server_conf.prototxt  
   |- serving_server_conf.stream.prototxt
 
-|- ppocr_det_mobile_2.0_client
+|- ppocr_det_v3_client
   |- serving_client_conf.prototxt  
   |- serving_client_conf.stream.prototxt
 
@@ -193,16 +202,13 @@ The recognition model is the same.
     2021-05-13 03:42:36,979         chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
     ```
 
+<a name="C++"></a>
 ## C++ Serving
 
 Service deployment based on python obviously has the advantage of convenient secondary development. However, the real application often needs to pursue better performance. PaddleServing also provides a more performant C++ deployment version.
 
 The C++ service deployment is the same as python in the environment setup and data preparation stages, the difference is when the service is started and the client sends requests.
 
-| Language | Speed ​​| Secondary development | Do you need to compile |
-|-----|-----|---------|------------|
-| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled |
-| python | general | easy | single-model/multi-model no compilation required |
 
 1. Compile Serving
 
@@ -211,7 +217,7 @@ The C++ service deployment is the same as python in the environment setup and da
 2. Run the following command to start the service.
     ```
     # Start the service and save the running log in log.txt
-    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
     ```
     After the service is successfully started, a log similar to the following will be printed in log.txt
     ![](./imgs/start_server.png)
@@ -219,7 +225,7 @@ The C++ service deployment is the same as python in the environment setup and da
 3. Send service request
 
    Due to the need for pre and post-processing in the C++Server part, in order to speed up the input to the C++Server is only the base64 encoded string of the picture, it needs to be manually modified
-   Change the feed_type field and shape field in ppocrv2_det_client/serving_client_conf.prototxt to the following:
+   Change the feed_type field and shape field in ppocr_det_v3_client/serving_client_conf.prototxt to the following:
 
    ```
     feed_var {
@@ -234,7 +240,7 @@ The C++ service deployment is the same as python in the environment setup and da
    start the client:
 
     ```
-    python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
+    python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
     ```
     After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
     ![](./imgs/results.png)  

deploy/pdserving/README_CN.md

@@ -9,13 +9,21 @@ PaddleOCR提供2种服务部署方式：
 
 # 基于PaddleServing的服务部署
 
-本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PP-OCR动态图模型的pipeline在线服务。
+本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md) 工具部署PP-OCR动态图模型的pipeline在线服务。
 
 相比较于hubserving部署，PaddleServing具备以下优点：
 - 支持客户端和服务端之间高并发和高效通信
 - 支持 工业级的服务能力 例如模型管理，在线加载，在线A/B测试等
 - 支持 多种编程语言 开发客户端，例如C++, Python和Java
 
+PaddleServing 支持多种语言部署，本例中提供了python pipeline 和 C++ 两种部署方式，两者的对比如下：
+
+| 语言 | 速度 | 二次开发 | 是否需要编译 |
+|-----|-----|---------|------------|
+| C++ | 很快 | 略有难度 | 单模型预测无需编译，多模型串联需要编译 |
+| python | 一般 | 容易 | 单模型/多模型 均无需编译|
+
+
 更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)。
 
 AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726)。
@@ -24,6 +32,7 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
 - [环境准备](#环境准备)
 - [模型转换](#模型转换)
 - [Paddle Serving pipeline部署](#部署)
+- [Paddle Serving C++部署](#C++)
 - [Windows用户](#Windows用户)
 - [FAQ](#FAQ)
 
@@ -34,26 +43,33 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
 
 - 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
 
+    ```
+    git clone https://github.com/PaddlePaddle/PaddleOCR
+
+    # 进入到工作目录
+    cd PaddleOCR/deploy/pdserving/
+    ```
+
 - 准备PaddleServing的运行环境，步骤如下
 
 ```bash
 # 安装serving，用于启动服务
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
-pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
+pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
 # 如果是cuda10.1环境，可以使用下面的命令安装paddle-serving-server
-# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
-# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
+# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
+# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
 
 # 安装client，用于向服务发送请求
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
-pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
+pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
 
 # 安装serving-app
-wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
-pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
+wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
+pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
 ```
 
-**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)。
+**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md)。
 
 <a name="模型转换"></a>
 ## 模型转换
@@ -64,38 +80,38 @@ pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
 
 ```bash
 # 下载并解压 OCR 文本检测模型
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
 # 下载并解压 OCR 文本识别模型
-wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar &&  tar -xf ch_PP-OCRv2_rec_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar &&  tar -xf ch_PP-OCRv3_rec_infer.tar
 ```
 
 接下来，用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
 
 ```bash
 # 转换检测模型
-python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
+python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
-                                         --serving_client ./ppocr_det_mobile_2.0_client/
+                                         --serving_server ./ppocr_det_v3_serving/ \
+                                         --serving_client ./ppocr_det_v3_client/
 
 # 转换识别模型
-python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
+python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
-                                         --serving_client ./ppocr_rec_mobile_2.0_client/
+                                         --serving_server ./ppocr_rec_v3_serving/  \
+                                         --serving_client ./ppocr_rec_v3_client/
 ```
 
-检测模型转换完成后，会在当前文件夹多出`ppocr_det_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹，具备如下格式：
+检测模型转换完成后，会在当前文件夹多出`ppocr_det_v3_serving` 和`ppocr_det_v3_client`的文件夹，具备如下格式：
 ```
-|- ppocr_det_mobile_2.0_serving/
+|- ppocr_det_v3_serving/
   |- __model__  
   |- __params__
   |- serving_server_conf.prototxt  
   |- serving_server_conf.stream.prototxt
 
-|- ppocr_det_mobile_2.0_client
+|- ppocr_det_v3_client
   |- serving_client_conf.prototxt  
   |- serving_client_conf.stream.prototxt
 
@@ -105,13 +121,8 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
 <a name="部署"></a>
 ## Paddle Serving pipeline部署
 
-1. 下载PaddleOCR代码，若已下载可跳过此步骤
-    ```
-    git clone https://github.com/PaddlePaddle/PaddleOCR
+1. 确认工作目录下文件结构：
 
-    # 进入到工作目录
-    cd PaddleOCR/deploy/pdserving/
-    ```
     pdserver目录包含启动pipeline服务和发送预测请求的代码，包括：
     ```
     __init__.py
@@ -196,16 +207,12 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
 
 C++ 服务部署在环境搭建和数据准备阶段与 python 相同，区别在于启动服务和客户端发送请求时不同。
 
-| 语言 | 速度 | 二次开发 | 是否需要编译 |
-|-----|-----|---------|------------|
-| C++ | 很快 | 略有难度 | 单模型预测无需编译，多模型串联需要编译 |
-| python | 一般 ｜ 容易 | 单模型/多模型 均无需编译|
-
 1. 准备 Serving 环境
 
 为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。
 
 首先需要下载Serving代码库, 把OCR文本检测预处理相关代码替换到Serving库中
+
 ```
 git clone https://github.com/PaddlePaddle/Serving
 
@@ -223,7 +230,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
 
     ```
     # 启动服务，运行日志保存在log.txt
-    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
     ```
     成功启动服务后，log.txt中会打印类似如下日志
     ![](./imgs/start_server.png)
@@ -231,7 +238,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
 3. 发送服务请求：
 
    由于需要在C++Server部分进行前后处理，为了加速传入C++Server的仅仅是图片的base64编码的字符串，故需要手动修改
-   ppocrv2_det_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段，修改成如下内容：
+   ppocr_det_v3_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段，修改成如下内容：
    ```
     feed_var {
     name: "x"
@@ -243,7 +250,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
    ```
    启动客户端
    ```
-    python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
+    python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
    ```
 
     成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：