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+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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+[English](README_CN.md) | 简体中文
+# PP-OCRv3 SOPHGO C++部署示例
+本目录下提供`infer.cc`快速完成PPOCRv3模型在SOPHGO BM1684x板子上加速部署的示例。
+
+## 1. 部署环境准备
+在部署前，需自行编译基于SOPHGO硬件的预测库，参考文档[SOPHGO硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
+
+## 2. 生成基本目录文件
+
+该例程由以下几个部分组成
+```text
+.
+├── CMakeLists.txt
+├── fastdeploy-sophgo  # 编译好的SDK文件夹
+├── image  # 存放图片的文件夹
+├── infer.cc
+└── model  # 存放模型文件的文件夹
+```
+
+## 3.部署示例
+
+### 3.1 下载部署示例代码
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/ocr/PP-OCR/sophgo/cpp
+
+# 如果您希望从PaddleOCR下载示例代码，请运行
+git clone https://github.com/PaddlePaddle/PaddleOCR.git
+# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
+git checkout dygraph
+cd PaddleOCR/deploy/fastdeploy/sophgo/cpp
+```
+
+### 3.2 拷贝bmodel模型文至model文件夹
+将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README.md). 将转换后的SOPHGO bmodel模型文件拷贝至model中.
+
+### 3.3 准备测试图片至image文件夹，以及字典文件
+```bash
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+cp 12.jpg image/
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+```
+
+### 3.4 编译example
+
+```bash
+cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-0.0.3
+make
+```
+
+### 3.5 运行例程
+
+```bash
+./infer_demo model ./ppocr_keys_v1.txt image/12.jpeg
+```
+
+
+## 4. 更多指南
+
+- [PP-OCR系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1ocr.html)
+- [FastDeploy部署PaddleOCR模型概览](../../)
+- [PP-OCRv3 Python部署](../python)
+- 如果用户想要调整前后处理超参数、单独使用文字检测识别模型、使用其他模型等，更多详细文档与说明请参考[PP-OCR系列在CPU/GPU上的部署](../../cpu-gpu/cpp/README.md)
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+// 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.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &det_model_dir,
+                  const std::string &rec_label_file,
+                  const std::string &image_file,
+                  const fastdeploy::RuntimeOption &option) {
+  auto det_model_file =
+      det_model_dir + sep + "ch_PP-OCRv3_det_1684x_f32.bmodel";
+  auto det_params_file = det_model_dir + sep + "";
+
+  auto cls_model_file =
+      det_model_dir + sep + "ch_ppocr_mobile_v2.0_cls_1684x_f32.bmodel";
+  auto cls_params_file = det_model_dir + sep + "";
+
+  auto rec_model_file =
+      det_model_dir + sep + "ch_PP-OCRv3_rec_1684x_f32.bmodel";
+  auto rec_params_file = det_model_dir + sep + "";
+
+  auto format = fastdeploy::ModelFormat::SOPHGO;
+
+  auto det_option = option;
+  auto cls_option = option;
+  auto rec_option = option;
+
+  // The cls and rec model can inference a batch of images now.
+  // User could initialize the inference batch size and set them after create
+  // PPOCR model.
+  int cls_batch_size = 1;
+  int rec_batch_size = 1;
+
+  // If use TRT backend, the dynamic shape will be set as follow.
+  // We recommend that users set the length and height of the detection model to
+  // a multiple of 32. We also recommend that users set the Trt input shape as
+  // follow.
+  det_option.SetTrtInputShape("x", {1, 3, 64, 64}, {1, 3, 640, 640},
+                              {1, 3, 960, 960});
+  cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {cls_batch_size, 3, 48, 320},
+                              {cls_batch_size, 3, 48, 1024});
+  rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {rec_batch_size, 3, 48, 320},
+                              {rec_batch_size, 3, 48, 2304});
+
+  // Users could save TRT cache file to disk as follow.
+  // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
+  // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
+  // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
+
+  auto det_model = fastdeploy::vision::ocr::DBDetector(
+      det_model_file, det_params_file, det_option, format);
+  auto cls_model = fastdeploy::vision::ocr::Classifier(
+      cls_model_file, cls_params_file, cls_option, format);
+  auto rec_model = fastdeploy::vision::ocr::Recognizer(
+      rec_model_file, rec_params_file, rec_label_file, rec_option, format);
+
+  // Users could enable static shape infer for rec model when deploy PP-OCR on
+  // hardware which can not support dynamic shape infer well, like Huawei Ascend
+  // series.
+  rec_model.GetPreprocessor().SetStaticShapeInfer(true);
+  rec_model.GetPreprocessor().SetRecImageShape({3, 48, 584});
+
+  assert(det_model.Initialized());
+  assert(cls_model.Initialized());
+  assert(rec_model.Initialized());
+
+  // 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);
+
+  // Set inference batch size for cls model and rec model, the value could be -1
+  // and 1 to positive infinity. When inference batch size is set to -1, it
+  // means that the inference batch size of the cls and rec models will be the
+  // same as the number of boxes detected by the det model.
+  ppocr_v3.SetClsBatchSize(cls_batch_size);
+  ppocr_v3.SetRecBatchSize(rec_batch_size);
+
+  if (!ppocr_v3.Initialized()) {
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::OCRResult 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::VisOcr(im_bak, result);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 4) {
+    std::cout << "Usage: infer_demo path/to/model "
+                 "path/to/rec_label_file path/to/image "
+                 "e.g ./infer_demo ./ocr_bmodel "
+                 "./ppocr_keys_v1.txt ./12.jpg"
+              << std::endl;
+    return -1;
+  }
+
+  fastdeploy::RuntimeOption option;
+  option.UseSophgo();
+  option.UseSophgoBackend();
+
+  std::string model_dir = argv[1];
+  std::string rec_label_file = argv[2];
+  std::string test_image = argv[3];
+  InitAndInfer(model_dir, rec_label_file, test_image, option);
+  return 0;
+}