[Doc] Rename PPOCRSystem to PPOCR and update comments. (#395)

* Imporve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Make all the model links come from PaddleOCR * Improve OCR readme * Improve OCR readme * Improve OCR readme * Improve OCR readme * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add comments to create API docs * Improve OCR comments * Rename OCR and add comments * Make sure previous python example works * Make sure previous python example works Co-authored-by: Jason <jiangjiajun@baidu.com>
2025-10-06 00:57:33 +08:00 · 2022-10-19 17:21:48 +08:00
parent 85e1c647f6
commit 24317e1a14
23 changed files with 380 additions and 186 deletions
--- a/docs/api_docs/python/ocr.md
+++ b/docs/api_docs/python/ocr.md
@@ -1,3 +1,41 @@
 # OCR API

-comming soon...
+## fastdeploy.vision.ocr.DBDetector
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.ocr.DBDetector
+    :members:
+    :inherited-members:
+```
+
+## fastdeploy.vision.ocr.Classifier
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.ocr.Classifier
+    :members:
+    :inherited-members:
+```
+
+## fastdeploy.vision.ocr.Recognizer
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.ocr.Recognizer
+    :members:
+    :inherited-members:
+```
+
+## fastdeploy.vision.ocr.PPOCRv2
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.ocr.PPOCRv2
+    :members:
+    :inherited-members:
+```
+
+## fastdeploy.vision.ocr.PPOCRv3
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.ocr.PPOCRv3
+    :members:
+    :inherited-members:
+```
--- a/docs/api_docs/python/requirements.txt
+++ b/docs/api_docs/python/requirements.txt
@@ -3,3 +3,4 @@ recommonmark
 sphinx_markdown_tables
 sphinx_rtd_theme
 furo
+myst_parser
--- a/examples/vision/ocr/PP-OCRv2/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv2/cpp/README.md
@@ -1,6 +1,6 @@
-# PPOCRSystemv2 C++部署示例
+# PPOCRv2 C++部署示例

-本目录下提供`infer.cc`快速完成PPOCRSystemv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+本目录下提供`infer.cc`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。

 在部署前，需确认以下两个步骤

@@ -19,14 +19,14 @@ make -j


 # 下载模型,图片和字典文件
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_det_infer.tar.gz
-tar -xvf ch_PP-OCRv2_det_infer.tar.gz
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar -xvf ch_PP-OCRv2_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
-tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
+wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar -xvf ch_PP-OCRv2_rec_infer.tar

 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg

@@ -48,17 +48,17 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">


-## PPOCRSystemv2 C++接口
+## PPOCRv2 C++接口

-### PPOCRSystemv2类
+### PPOCRv2类

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Classifier* cls_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```

-PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
+PPOCRv2 的初始化，由检测，分类和识别模型串联构成

 **参数**

@@ -67,10 +67,10 @@ PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
 > * **Recognizer**(model): OCR中的识别模型

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```
-PPOCRSystemv2 的初始化，由检测，识别模型串联构成(无分类器)
+PPOCRv2 的初始化，由检测，识别模型串联构成(无分类器)

 **参数**

--- a/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
@@ -37,12 +37,12 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());
  
-  // The classification model is optional, so the OCR system can also be connected in series as follows
-  // auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &rec_model);
-  auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &cls_model, &rec_model);
+  // The classification model is optional, so the PP-OCR can also be connected in series as follows
+  // auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &rec_model);
+  auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &cls_model, &rec_model);

-  if(!ocr_system_v2.Initialized()){
-    std::cerr << "Failed to initialize OCR system." << std::endl;
+  if(!ppocr_v2.Initialized()){
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
    return;
  }

@@ -50,14 +50,14 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  auto im_bak = im.clone();
  
  fastdeploy::vision::OCRResult result;
-  if (!ocr_system_v2.Predict(&im, &result)) {
+  if (!ppocr_v2.Predict(&im, &result)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << result.Str() << std::endl;

-  auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
+  auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
--- a/examples/vision/ocr/PP-OCRv2/python/README.md
+++ b/examples/vision/ocr/PP-OCRv2/python/README.md
@@ -1,23 +1,23 @@
-# PPOCRSystemv2 Python部署示例
+# PPOCRv2 Python部署示例

 在部署前，需确认以下两个步骤

 - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
 - 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)

-本目录下提供`infer.py`快速完成PPOCRSystemv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+本目录下提供`infer.py`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

 ```

 # 下载模型,图片和字典文件
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_det_infer.tar.gz
-tar -xvf ch_PP-OCRv2_det_infer.tar.gz
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar -xvf ch_PP-OCRv2_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
-tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
+wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar -xvf ch_PP-OCRv2_rec_infer.tar

 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg

@@ -39,12 +39,12 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
 运行完成可视化结果如下图所示
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">

-## PPOCRSystemv2 Python接口
+## PPOCRv2 Python接口

 ```
-fd.vision.ocr.PPOCRSystemv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+fd.vision.ocr.PPOCRv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
 ```
-PPOCRSystemv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
+PPOCRv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None

 **参数**

@@ -55,7 +55,7 @@ PPOCRSystemv2的初始化,输入的参数是检测模型，分类模型和识别
 ### predict函数

 > ```
-> result = ocr_system.predict(im)
+> result = ppocr_v2.predict(im)
 > ```
 >
 > 模型预测接口，输入是一张图片
--- a/examples/vision/ocr/PP-OCRv2/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv2/python/infer.py
@@ -110,15 +110,15 @@ rec_model = fd.vision.ocr.Recognizer(
    rec_label_file,
    runtime_option=runtime_option)

-# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
-ocr_system = fd.vision.ocr.PPOCRSystemv2(
+# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
+ppocr_v2 = fd.vision.ocr.PPOCRv2(
    det_model=det_model, cls_model=cls_model, rec_model=rec_model)

 # 预测图片准备
 im = cv2.imread(args.image)

 #预测并打印结果
-result = ocr_system.predict(im)
+result = ppocr_v2.predict(im)

 print(result)

--- a/examples/vision/ocr/PP-OCRv3/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv3/cpp/README.md
@@ -1,6 +1,6 @@
-# PPOCRSystemv3 C++部署示例
+# PPOCRv3 C++部署示例

-本目录下提供`infer.cc`快速完成PPOCRSystemv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+本目录下提供`infer.cc`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。

 在部署前，需确认以下两个步骤

@@ -22,8 +22,8 @@ make -j
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
 tar -xvf ch_PP-OCRv3_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 tar -xvf ch_PP-OCRv3_rec_infer.tar
@@ -48,17 +48,17 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">


-## PPOCRSystemv3 C++接口
+## PPOCRv3 C++接口

-### PPOCRSystemv3类
+### PPOCRv3类

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Classifier* cls_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```

-PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
+PPOCRv3 的初始化，由检测，分类和识别模型串联构成

 **参数**

@@ -67,10 +67,10 @@ PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
 > * **Recognizer**(model): OCR中的识别模型

 ```
-fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
+fastdeploy::pipeline::PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```
-PPOCRSystemv3 的初始化，由检测，识别模型串联构成(无分类器)
+PPOCRv3 的初始化，由检测，识别模型串联构成(无分类器)

 **参数**

--- a/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
@@ -37,12 +37,12 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());

-  // The classification model is optional, so the OCR system can also be connected in series as follows
-  // auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &rec_model);
-  auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &cls_model, &rec_model);
+  // The classification model is optional, so the PP-OCR can also be connected in series as follows
+  // auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
+  auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);

-  if(!ocr_system_v3.Initialized()){
-    std::cerr << "Failed to initialize OCR system." << std::endl;
+  if(!ppocr_v3.Initialized()){
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
    return;
  }

@@ -50,14 +50,14 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  auto im_bak = im.clone();
  
  fastdeploy::vision::OCRResult result;
-  if (!ocr_system_v3.Predict(&im, &result)) {
+  if (!ppocr_v3.Predict(&im, &result)) {
    std::cerr << "Failed to predict." << std::endl;
    return;
  }

  std::cout << result.Str() << std::endl;

-  auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
+  auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
  cv::imwrite("vis_result.jpg", vis_im);
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
--- a/examples/vision/ocr/PP-OCRv3/python/README.md
+++ b/examples/vision/ocr/PP-OCRv3/python/README.md
@@ -1,11 +1,11 @@
-# PPOCRSystemv3 Python部署示例
+# PPOCRv3 Python部署示例

 在部署前，需确认以下两个步骤

 - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
 - 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)

-本目录下提供`infer.py`快速完成PPOCRSystemv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+本目录下提供`infer.py`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

 ```

@@ -13,8 +13,8 @@
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
 tar xvf ch_PP-OCRv3_det_infer.tar

-wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
-tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
+https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar

 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 tar xvf ch_PP-OCRv3_rec_infer.tar
@@ -38,12 +38,12 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
 运行完成可视化结果如下图所示
 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">

-## PPOCRSystemv3 Python接口
+## PPOCRv3 Python接口

 ```
-fd.vision.ocr.PPOCRSystemv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+fd.vision.ocr.PPOCRv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
 ```
-PPOCRSystemv3的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
+PPOCRv3的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None

 **参数**

@@ -54,7 +54,7 @@ PPOCRSystemv3的初始化,输入的参数是检测模型，分类模型和识别
 ### predict函数

 > ```
-> result = ocr_system.predict(im)
+> result = ppocr_v3.predict(im)
 > ```
 >
 > 模型预测接口，输入是一张图片
--- a/examples/vision/ocr/PP-OCRv3/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv3/python/infer.py
@@ -110,15 +110,15 @@ rec_model = fd.vision.ocr.Recognizer(
    rec_label_file,
    runtime_option=runtime_option)

-# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
-ocr_system = fd.vision.ocr.PPOCRSystemv3(
+# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
+ppocr_v3 = fd.vision.ocr.PPOCRv3(
    det_model=det_model, cls_model=cls_model, rec_model=rec_model)

 # 预测图片准备
 im = cv2.imread(args.image)

 #预测并打印结果
-result = ocr_system.predict(im)
+result = ppocr_v3.predict(im)

 print(result)

--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -42,8 +42,8 @@
 #include "fastdeploy/vision/matting/ppmatting/ppmatting.h"
 #include "fastdeploy/vision/ocr/ppocr/classifier.h"
 #include "fastdeploy/vision/ocr/ppocr/dbdetector.h"
-#include "fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h"
-#include "fastdeploy/vision/ocr/ppocr/ppocr_system_v3.h"
+#include "fastdeploy/vision/ocr/ppocr/ppocr_v2.h"
+#include "fastdeploy/vision/ocr/ppocr/ppocr_v3.h"
 #include "fastdeploy/vision/ocr/ppocr/recognizer.h"
 #include "fastdeploy/vision/segmentation/ppseg/model.h"
 #endif
--- a/fastdeploy/vision/ocr/ocr_pybind.cc
+++ b/fastdeploy/vision/ocr/ocr_pybind.cc
@@ -17,13 +17,13 @@
 namespace fastdeploy {

 void BindPPOCRModel(pybind11::module& m);
-void BindPPOCRSystemv3(pybind11::module& m);
-void BindPPOCRSystemv2(pybind11::module& m);
+void BindPPOCRv3(pybind11::module& m);
+void BindPPOCRv2(pybind11::module& m);

 void BindOcr(pybind11::module& m) {
  auto ocr_module = m.def_submodule("ocr", "Module to deploy OCR models");
  BindPPOCRModel(ocr_module);
-  BindPPOCRSystemv3(ocr_module);
-  BindPPOCRSystemv2(ocr_module);
+  BindPPOCRv3(ocr_module);
+  BindPPOCRv2(ocr_module);
 }
 }  // namespace fastdeploy
--- a/fastdeploy/vision/ocr/ppocr/classifier.h
+++ b/fastdeploy/vision/ocr/ppocr/classifier.h
@@ -20,20 +20,36 @@

 namespace fastdeploy {
 namespace vision {
+/** \brief All OCR series model APIs are defined inside this namespace
+ *
+ */
 namespace ocr {
-
+/*! @brief Classifier object is used to load the classification model provided by PaddleOCR.
+ */
 class FASTDEPLOY_DECL Classifier : public FastDeployModel {
 public:
  Classifier();
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
+   * \param[in] model_format Model format of the loaded model, default is Paddle format.
+   */
  Classifier(const std::string& model_file, const std::string& params_file = "",
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::PADDLE);
-
+  /// Get model's name
  std::string ModelName() const { return "ppocr/ocr_cls"; }
-
+  /** \brief Predict the input image and get OCR classification model result.
+   *
+   * \param[in] im The input image data, comes from cv::imread().
+   * \param[in] result The output of OCR classification model result will be writen to this structure.
+   * \return true if the prediction is successed, otherwise false.
+   */
  virtual bool Predict(cv::Mat* img, std::tuple<int, float>* result);

-  // pre & post parameters
+  // Pre & Post parameters
  float cls_thresh;
  std::vector<int> cls_image_shape;
  int cls_batch_num;
@@ -44,9 +60,9 @@ class FASTDEPLOY_DECL Classifier : public FastDeployModel {

 private:
  bool Initialize();
-
+  /// Preprocess the input data, and set the preprocessed results to `outputs`
  bool Preprocess(Mat* img, FDTensor* output);
-
+  /// Postprocess the inferenced results, and set the final result to `result`
  bool Postprocess(FDTensor& infer_result, std::tuple<int, float>* result);
 };

--- a/fastdeploy/vision/ocr/ppocr/dbdetector.h
+++ b/fastdeploy/vision/ocr/ppocr/dbdetector.h
@@ -20,22 +20,38 @@

 namespace fastdeploy {
 namespace vision {
+/** \brief All OCR series model APIs are defined inside this namespace
+ *
+ */
 namespace ocr {

+/*! @brief DBDetector object is used to load the detection model provided by PaddleOCR.
+ */
 class FASTDEPLOY_DECL DBDetector : public FastDeployModel {
 public:
  DBDetector();
-
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
+   * \param[in] model_format Model format of the loaded model, default is Paddle format.
+   */
  DBDetector(const std::string& model_file, const std::string& params_file = "",
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::PADDLE);
-
+  /// Get model's name
  std::string ModelName() const { return "ppocr/ocr_det"; }
-
+  /** \brief Predict the input image and get OCR detection model result.
+   *
+   * \param[in] im The input image data, comes from cv::imread().
+   * \param[in] boxes_result The output of OCR detection model result will be writen to this structure.
+   * \return true if the prediction is successed, otherwise false.
+   */
  virtual bool Predict(cv::Mat* im,
                       std::vector<std::array<int, 8>>* boxes_result);

-  // pre&post process parameters
+  // Pre & Post process parameters
  int max_side_len;

  float ratio_h{};
@@ -53,14 +69,14 @@ class FASTDEPLOY_DECL DBDetector : public FastDeployModel {

 private:
  bool Initialize();
-
+  /// Preprocess the input data, and set the preprocessed results to `outputs`
  bool Preprocess(Mat* mat, FDTensor* outputs,
                  std::map<std::string, std::array<float, 2>>* im_info);
-
+  /*! @brief Postprocess the inferenced results, and set the final result to `boxes_result`
+   */
  bool Postprocess(FDTensor& infer_result,
                   std::vector<std::array<int, 8>>* boxes_result,
                   const std::map<std::string, std::array<float, 2>>& im_info);
-
  PostProcessor post_processor_;
 };

--- a/fastdeploy/vision/ocr/ppocr/ocrsys_pybind.cc
+++ b/fastdeploy/vision/ocr/ppocr/ocrsys_pybind.cc
@@ -15,17 +15,17 @@
 #include "fastdeploy/pybind/main.h"

 namespace fastdeploy {
-void BindPPOCRSystemv3(pybind11::module& m) {
-  // OCRSys
-  pybind11::class_<application::ocrsystem::PPOCRSystemv3, FastDeployModel>(
-      m, "PPOCRSystemv3")
+void BindPPOCRv3(pybind11::module& m) {
+  // PPOCRv3
+  pybind11::class_<pipeline::PPOCRv3, FastDeployModel>(
+      m, "PPOCRv3")

      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Classifier*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Recognizer*>())
-      .def("predict", [](application::ocrsystem::PPOCRSystemv3& self,
+      .def("predict", [](pipeline::PPOCRv3& self,
                         pybind11::array& data) {
        auto mat = PyArrayToCvMat(data);
        vision::OCRResult res;
@@ -34,16 +34,16 @@ void BindPPOCRSystemv3(pybind11::module& m) {
      });
 }

-void BindPPOCRSystemv2(pybind11::module& m) {
-  // OCRSys
-  pybind11::class_<application::ocrsystem::PPOCRSystemv2, FastDeployModel>(
-      m, "PPOCRSystemv2")
+void BindPPOCRv2(pybind11::module& m) {
+  // PPOCRv2
+  pybind11::class_<pipeline::PPOCRv2, FastDeployModel>(
+      m, "PPOCRv2")
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Classifier*,
                          fastdeploy::vision::ocr::Recognizer*>())
      .def(pybind11::init<fastdeploy::vision::ocr::DBDetector*,
                          fastdeploy::vision::ocr::Recognizer*>())
-      .def("predict", [](application::ocrsystem::PPOCRSystemv2& self,
+      .def("predict", [](pipeline::PPOCRv2& self,
                         pybind11::array& data) {
        auto mat = PyArrayToCvMat(data);
        vision::OCRResult res;
--- a/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.h
+++ b/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.h
@@ -1,40 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-
-#include "fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h"
-
-namespace fastdeploy {
-namespace application {
-namespace ocrsystem {
-
-class FASTDEPLOY_DECL PPOCRSystemv3 : public PPOCRSystemv2 {
- public:
-  PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
-                fastdeploy::vision::ocr::Classifier* cls_model,
-                fastdeploy::vision::ocr::Recognizer* rec_model) : PPOCRSystemv2(det_model, cls_model, rec_model) {
-    // The only difference between v2 and v3
-    recognizer_->rec_image_shape[1] = 48;
-  }
-
-  PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
-                fastdeploy::vision::ocr::Recognizer* rec_model) : PPOCRSystemv2(det_model, rec_model) {
-    recognizer_->rec_image_shape[1] = 48;
-  }
-};
-
-}  // namespace ocrsystem
-}  // namespace application
-}  // namespace fastdeploy
--- a/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc
+++ b/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc
@@ -12,27 +12,26 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#include "fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h"
+#include "fastdeploy/vision/ocr/ppocr/ppocr_v2.h"
 #include "fastdeploy/utils/perf.h"
 #include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"

 namespace fastdeploy {
-namespace application {
-namespace ocrsystem {
-PPOCRSystemv2::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+namespace pipeline {
+PPOCRv2::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                             fastdeploy::vision::ocr::Classifier* cls_model,
                             fastdeploy::vision::ocr::Recognizer* rec_model)
    : detector_(det_model), classifier_(cls_model), recognizer_(rec_model) {
  recognizer_->rec_image_shape[1] = 32;
 }

-PPOCRSystemv2::PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+PPOCRv2::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                             fastdeploy::vision::ocr::Recognizer* rec_model)
    : detector_(det_model), recognizer_(rec_model) {
  recognizer_->rec_image_shape[1] = 32;
 }

-bool PPOCRSystemv2::Initialized() const {
+bool PPOCRv2::Initialized() const {
  
  if (detector_ != nullptr && !detector_->Initialized()){
    return false;
@@ -48,21 +47,21 @@ bool PPOCRSystemv2::Initialized() const {
  return true; 
 }

-bool PPOCRSystemv2::Detect(cv::Mat* img,
+bool PPOCRv2::Detect(cv::Mat* img,
                           fastdeploy::vision::OCRResult* result) {
  if (!detector_->Predict(img, &(result->boxes))) {
-    FDERROR << "There's error while detecting image in PPOCRSystem." << std::endl;
+    FDERROR << "There's error while detecting image in PPOCR." << std::endl;
    return false;
  }
  vision::ocr::SortBoxes(result);
  return true;
 }

-bool PPOCRSystemv2::Recognize(cv::Mat* img,
+bool PPOCRv2::Recognize(cv::Mat* img,
                              fastdeploy::vision::OCRResult* result) {
  std::tuple<std::string, float> rec_result;
  if (!recognizer_->Predict(img, &rec_result)) {
-    FDERROR << "There's error while recognizing image in PPOCRSystem." << std::endl;
+    FDERROR << "There's error while recognizing image in PPOCR." << std::endl;
    return false;
  }

@@ -71,12 +70,12 @@ bool PPOCRSystemv2::Recognize(cv::Mat* img,
  return true;
 }

-bool PPOCRSystemv2::Classify(cv::Mat* img,
+bool PPOCRv2::Classify(cv::Mat* img,
                             fastdeploy::vision::OCRResult* result) {
  std::tuple<int, float> cls_result;

  if (!classifier_->Predict(img, &cls_result)) {
-    FDERROR << "There's error while classifying image in PPOCRSystem." << std::endl;
+    FDERROR << "There's error while classifying image in PPOCR." << std::endl;
    return false;
  }

@@ -85,7 +84,7 @@ bool PPOCRSystemv2::Classify(cv::Mat* img,
  return true;
 }

-bool PPOCRSystemv2::Predict(cv::Mat* img,
+bool PPOCRv2::Predict(cv::Mat* img,
                            fastdeploy::vision::OCRResult* result) {
  result->Clear();
  if (nullptr != detector_ && !Detect(img, result)) {
@@ -120,6 +119,5 @@ bool PPOCRSystemv2::Predict(cv::Mat* img,
  return true;
 };

-}  // namesapce ocrsystem
-}  // namespace application
+}  // namesapce pipeline
 }  // namespace fastdeploy
--- a/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h
+++ b/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.h
@@ -26,18 +26,38 @@
 #include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"

 namespace fastdeploy {
-namespace application {
-namespace ocrsystem {
-
-class FASTDEPLOY_DECL PPOCRSystemv2 : public FastDeployModel {
+/** \brief This pipeline can launch detection model, classification model and recognition model sequentially. All OCR pipeline APIs are defined inside this namespace.
+ *
+ */
+namespace pipeline {
+/*! @brief PPOCRv2 is used to load PP-OCRv2 series models provided by PaddleOCR.
+ */
+class FASTDEPLOY_DECL PPOCRv2 : public FastDeployModel {
 public:
-  PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+  /** \brief Set up the detection model path, classification model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
+   * \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
+   */
+  PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Classifier* cls_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);

-  PPOCRSystemv2(fastdeploy::vision::ocr::DBDetector* det_model,
+  /** \brief Classification model is optional, so this function is set up the detection model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
+   */
+  PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);

+  /** \brief Predict the input image and get OCR result.
+   *
+   * \param[in] im The input image data, comes from cv::imread().
+   * \param[in] result The output OCR result will be writen to this structure.
+   * \return true if the prediction successed, otherwise false.
+   */
  virtual bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
  bool Initialized() const override;

@@ -45,12 +65,19 @@ class FASTDEPLOY_DECL PPOCRSystemv2 : public FastDeployModel {
  fastdeploy::vision::ocr::DBDetector* detector_ = nullptr;
  fastdeploy::vision::ocr::Classifier* classifier_ = nullptr;
  fastdeploy::vision::ocr::Recognizer* recognizer_ = nullptr;
-
+  /// Launch the detection process in OCR.
  virtual bool Detect(cv::Mat* img, fastdeploy::vision::OCRResult* result);
+  /// Launch the recognition process in OCR.
  virtual bool Recognize(cv::Mat* img, fastdeploy::vision::OCRResult* result);
+  /// Launch the classification process in OCR.
  virtual bool Classify(cv::Mat* img, fastdeploy::vision::OCRResult* result);
 };

+namespace application {
+namespace ocrsystem {
+  typedef pipeline::PPOCRv2 PPOCRSystemv2;
 }  // namespace ocrsystem
 }  // namespace application
+
+}  // namespace pipeline
 }  // namespace fastdeploy
--- a/fastdeploy/vision/ocr/ppocr/ppocr_v3.h
+++ b/fastdeploy/vision/ocr/ppocr/ppocr_v3.h
@@ -0,0 +1,62 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "fastdeploy/vision/ocr/ppocr/ppocr_v2.h"
+
+namespace fastdeploy {
+/** \brief This pipeline can launch detection model, classification model and recognition model sequentially. All OCR pipeline APIs are defined inside this namespace.
+ *
+ */
+namespace pipeline {
+/*! @brief PPOCRv3 is used to load PP-OCRv3 series models provided by PaddleOCR.
+ */
+class FASTDEPLOY_DECL PPOCRv3 : public PPOCRv2 {
+ public:
+   /** \brief Set up the detection model path, classification model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv3_det_infer
+   * \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv3_rec_infer
+   */
+  PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
+                fastdeploy::vision::ocr::Classifier* cls_model,
+                fastdeploy::vision::ocr::Recognizer* rec_model)
+                : PPOCRv2(det_model, cls_model, rec_model) {
+    // The only difference between v2 and v3
+    recognizer_->rec_image_shape[1] = 48;
+  }
+  /** \brief Classification model is optional, so this function is set up the detection model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv3_det_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv3_rec_infer
+   */
+  PPOCRv3(fastdeploy::vision::ocr::DBDetector* det_model,
+                fastdeploy::vision::ocr::Recognizer* rec_model)
+                : PPOCRv2(det_model, rec_model) {
+    // The only difference between v2 and v3
+    recognizer_->rec_image_shape[1] = 48;
+  }
+};
+
+}  // namespace pipeline
+
+namespace application {
+namespace ocrsystem {
+  typedef pipeline::PPOCRv3 PPOCRSystemv3;
+}  // namespace ocrsystem
+}  // namespace application
+
+}  // namespace fastdeploy
--- a/fastdeploy/vision/ocr/ppocr/recognizer.cc
+++ b/fastdeploy/vision/ocr/ppocr/recognizer.cc
@@ -56,9 +56,6 @@ Recognizer::Recognizer(const std::string& model_file,
  runtime_option.model_format = model_format;
  runtime_option.model_file = model_file;
  runtime_option.params_file = params_file;
-  runtime_option.DeletePaddleBackendPass("matmul_transpose_reshape_fuse_pass");
-  runtime_option.DeletePaddleBackendPass(
-      "matmul_transpose_reshape_mkldnn_fuse_pass");

  initialized = Initialize();

--- a/fastdeploy/vision/ocr/ppocr/recognizer.h
+++ b/fastdeploy/vision/ocr/ppocr/recognizer.h
@@ -20,22 +20,39 @@

 namespace fastdeploy {
 namespace vision {
+/** \brief All OCR series model APIs are defined inside this namespace
+ *
+ */
 namespace ocr {
-
+/*! @brief Recognizer object is used to load the recognition model provided by PaddleOCR.
+ */
 class FASTDEPLOY_DECL Recognizer : public FastDeployModel {
 public:
  Recognizer();
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+   * \param[in] label_path Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
+   * \param[in] model_format Model format of the loaded model, default is Paddle format.
+   */
  Recognizer(const std::string& model_file, const std::string& params_file = "",
             const std::string& label_path = "",
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::PADDLE);
-
+  /// Get model's name
  std::string ModelName() const { return "ppocr/ocr_rec"; }
-
+  /** \brief Predict the input image and get OCR recognition model result.
+   *
+   * \param[in] im The input image data, comes from cv::imread().
+   * \param[in] rec_result The output of OCR recognition model result will be writen to this structure.
+   * \return true if the prediction is successed, otherwise false.
+   */
  virtual bool Predict(cv::Mat* img,
                       std::tuple<std::string, float>* rec_result);

-  // pre & post parameters
+  // Pre & Post parameters
  std::vector<std::string> label_list;
  int rec_batch_num;
  int rec_img_h;
@@ -48,10 +65,11 @@ class FASTDEPLOY_DECL Recognizer : public FastDeployModel {

 private:
  bool Initialize();
-
+  /// Preprocess the input data, and set the preprocessed results to `outputs`
  bool Preprocess(Mat* img, FDTensor* outputs,
                  const std::vector<int>& rec_image_shape);
-
+  /*! @brief Postprocess the inferenced results, and set the final result to `rec_result`
+   */
  bool Postprocess(FDTensor& infer_result,
                   std::tuple<std::string, float>* rec_result);
 };
--- a/python/fastdeploy/vision/ocr/init.py
+++ b/python/fastdeploy/vision/ocr/init.py
@@ -13,6 +13,8 @@
 # limitations under the License.
 from __future__ import absolute_import

+from .ppocr import PPOCRv3
+from .ppocr import PPOCRv2
 from .ppocr import PPOCRSystemv3
 from .ppocr import PPOCRSystemv2
 from .ppocr import DBDetector
--- a/python/fastdeploy/vision/ocr/ppocr/init.py
+++ b/python/fastdeploy/vision/ocr/ppocr/init.py
@@ -24,8 +24,13 @@ class DBDetector(FastDeployModel):
                 params_file="",
                 runtime_option=None,
                 model_format=ModelFormat.PADDLE):
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
+        """Load OCR detection model provided by PaddleOCR.
+
+        :param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
+        :param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
+        """
        super(DBDetector, self).__init__(runtime_option)

        if (len(model_file) == 0):
@@ -33,7 +38,6 @@ class DBDetector(FastDeployModel):
        else:
            self._model = C.vision.ocr.DBDetector(
                model_file, params_file, self._runtime_option, model_format)
-            # 通过self.initialized判断整个模型的初始化是否成功
            assert self.initialized, "DBDetector initialize failed."

    # 一些跟DBDetector模型有关的属性封装
@@ -81,8 +85,8 @@ class DBDetector(FastDeployModel):
    @det_db_box_thresh.setter
    def det_db_box_thresh(self, value):
        assert isinstance(
-            value,
-            float), "The value to set `det_db_box_thresh` must be type of float."
+            value, float
+        ), "The value to set `det_db_box_thresh` must be type of float."
        self._model.det_db_box_thresh = value

    @det_db_unclip_ratio.setter
@@ -119,8 +123,13 @@ class Classifier(FastDeployModel):
                 params_file="",
                 runtime_option=None,
                 model_format=ModelFormat.PADDLE):
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
+        """Load OCR classification model provided by PaddleOCR.
+
+        :param model_file: (str)Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
+        :param params_file: (str)Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
+        """
        super(Classifier, self).__init__(runtime_option)

        if (len(model_file) == 0):
@@ -128,7 +137,6 @@ class Classifier(FastDeployModel):
        else:
            self._model = C.vision.ocr.Classifier(
                model_file, params_file, self._runtime_option, model_format)
-            # 通过self.initialized判断整个模型的初始化是否成功
            assert self.initialized, "Classifier initialize failed."

    @property
@@ -159,7 +167,8 @@ class Classifier(FastDeployModel):
    @cls_batch_num.setter
    def cls_batch_num(self, value):
        assert isinstance(
-            value, int), "The value to set `cls_batch_num` must be type of int."
+            value,
+            int), "The value to set `cls_batch_num` must be type of int."
        self._model.cls_batch_num = value


@@ -170,8 +179,14 @@ class Recognizer(FastDeployModel):
                 label_path="",
                 runtime_option=None,
                 model_format=ModelFormat.PADDLE):
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
+        """Load OCR recognition model provided by PaddleOCR
+
+        :param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
+        :param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+        :param label_path: (str)Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
+        """
        super(Recognizer, self).__init__(runtime_option)

        if (len(model_file) == 0):
@@ -180,7 +195,6 @@ class Recognizer(FastDeployModel):
            self._model = C.vision.ocr.Recognizer(
                model_file, params_file, label_path, self._runtime_option,
                model_format)
-            # 通过self.initialized判断整个模型的初始化是否成功
            assert self.initialized, "Recognizer initialize failed."

    @property
@@ -210,33 +224,78 @@ class Recognizer(FastDeployModel):
    @rec_batch_num.setter
    def rec_batch_num(self, value):
        assert isinstance(
-            value, int), "The value to set `rec_batch_num` must be type of int."
+            value,
+            int), "The value to set `rec_batch_num` must be type of int."
        self._model.rec_batch_num = value


-class PPOCRSystemv3(FastDeployModel):
+class PPOCRv3(FastDeployModel):
    def __init__(self, det_model=None, cls_model=None, rec_model=None):
+        """Consruct a pipeline with text detector, direction classifier and text recognizer models
+
+        :param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
+        :param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
+        :param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
+        """
        assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
        if cls_model is None:
-            self.system = C.vision.ocr.PPOCRSystemv3(det_model._model,
+            self.system = C.vision.ocr.PPOCRv3(det_model._model,
                                               rec_model._model)
        else:
-            self.system = C.vision.ocr.PPOCRSystemv3(
+            self.system = C.vision.ocr.PPOCRv3(
                det_model._model, cls_model._model, rec_model._model)

    def predict(self, input_image):
+        """Predict an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: OCRResult
+        """
        return self.system.predict(input_image)


-class PPOCRSystemv2(FastDeployModel):
+class PPOCRSystemv3(PPOCRv3):
    def __init__(self, det_model=None, cls_model=None, rec_model=None):
+        logging.warning(
+            "DEPRECATED: fd.vision.ocr.PPOCRSystemv3 is deprecated, "
+            "please use fd.vision.ocr.PPOCRv3 instead.")
+        super(PPOCRSystemv3, self).__init__(det_model, cls_model, rec_model)
+
+    def predict(self, input_image):
+        return super(PPOCRSystemv3, self).predict(input_image)
+
+
+class PPOCRv2(FastDeployModel):
+    def __init__(self, det_model=None, cls_model=None, rec_model=None):
+        """Consruct a pipeline with text detector, direction classifier and text recognizer models
+
+        :param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
+        :param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
+        :param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
+        """
        assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
        if cls_model is None:
-            self.system = C.vision.ocr.PPOCRSystemv2(det_model._model,
+            self.system = C.vision.ocr.PPOCRv2(det_model._model,
                                               rec_model._model)
        else:
-            self.system = C.vision.ocr.PPOCRSystemv2(
+            self.system = C.vision.ocr.PPOCRv2(
                det_model._model, cls_model._model, rec_model._model)

    def predict(self, input_image):
+        """Predict an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: OCRResult
+        """
        return self.system.predict(input_image)
+
+
+class PPOCRSystemv2(PPOCRv2):
+    def __init__(self, det_model=None, cls_model=None, rec_model=None):
+        logging.warning(
+            "DEPRECATED: fd.vision.ocr.PPOCRSystemv2 is deprecated, "
+            "please use fd.vision.ocr.PPOCRv2 instead.")
+        super(PPOCRSystemv2, self).__init__(det_model, cls_model, rec_model)
+
+    def predict(self, input_image):
+        return super(PPOCRSystemv2, self).predict(input_image)