Merge branch 'dygraph' into dygraph

191c9dee · Evezerest · GitHub · 3c6d5512 · 8def6786 · 191c9dee
Unverified Commit 191c9dee authored Mar 18, 2022 by Evezerest Committed by GitHub Mar 18, 2022
20 changed files
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@@ -78,7 +78,7 @@ opencv3/
 #### 1.2.1 Direct download and installation
-[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
+[Paddle inference library official website](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux). You can review and select the appropriate version of the inference library on the official website.
 * After downloading, use the following command to extract files.
@@ -231,7 +231,7 @@ More parameters are as follows,
 |gpu_id|int|0|GPU id when use_gpu is true|
 |gpu_mem|int|4000|GPU memory requested|
 |cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
-|use_mkldnn|bool|true|Whether to use mkdlnn library|
+|enable_mkldnn|bool|true|Whether to use mkdlnn library|
 - Detection related parameters

--- a/deploy/cpp_infer/src/main.cpp
+++ b/deploy/cpp_infer/src/main.cpp
@@ -28,14 +28,14 @@
 #include <numeric>
 #include <glog/logging.h>
-#include <include/ocr_det.h>
 #include <include/ocr_cls.h>
+#include <include/ocr_det.h>
 #include <include/ocr_rec.h>
 #include <include/utility.h>
 #include <sys/stat.h>
-#include <gflags/gflags.h>
 #include "auto_log/autolog.h"
+#include <gflags/gflags.h>
 DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
 DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
@@ -51,9 +51,10 @@ DEFINE_string(image_dir, "", "Dir of input image.");
 DEFINE_string(det_model_dir, "", "Path of det inference model.");
 DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
 DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
-DEFINE_double(det_db_box_thresh, 0.5, "Threshold of det_db_box_thresh.");
+DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
-DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio.");
+DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
 DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
+DEFINE_bool(use_dilation, false, "Whether use the dilation on output map.");
 DEFINE_bool(visualize, true, "Whether show the detection results.");
 // classification related
 DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
@@ -62,15 +63,14 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
 // recognition related
 DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
 DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
-DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
+DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt",
+              "Path of dictionary.");
 using namespace std;
 using namespace cv;
 using namespace PaddleOCR;
+static bool PathExists(const std::string &path) {
-static bool PathExists(const std::string& path){
 #ifdef _WIN32
  struct _stat buffer;
  return (_stat(path.c_str(), &buffer) == 0);
@@ -80,22 +80,22 @@ static bool PathExists(const std::string& path){
 #endif // !_WIN32
 }
 int main_det(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info = {0, 0, 0};
  DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                   FLAGS_gpu_mem, FLAGS_cpu_threads, 
+                 FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                   FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
+                 FLAGS_max_side_len, FLAGS_det_db_thresh,
                 FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
-                   FLAGS_use_polygon_score, FLAGS_visualize,
+                 FLAGS_use_polygon_score, FLAGS_use_dilation, FLAGS_visualize,
                 FLAGS_use_tensorrt, FLAGS_precision);
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-//       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
+    //       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
-        std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << endl;
      exit(1);
    }
    std::vector<std::vector<std::vector<int>>> boxes;
@@ -119,22 +119,14 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
  }
  if (FLAGS_benchmark) {
-        AutoLogger autolog("ocr_det", 
+    AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
-                           FLAGS_use_gpu,
+                       FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
-                           FLAGS_use_tensorrt,
+                       FLAGS_precision, time_info, cv_all_img_names.size());
-                           FLAGS_enable_mkldnn,
-                           FLAGS_cpu_threads,
-                           1, 
-                           "dynamic", 
-                           FLAGS_precision, 
-                           time_info, 
-                           cv_all_img_names.size());
    autolog.report();
  }
  return 0;
 }
 int main_rec(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info = {0, 0, 0};
@@ -144,9 +136,9 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
  cout << "label file: " << char_list_file << endl;
  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                       FLAGS_gpu_mem, FLAGS_cpu_threads,
+                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                       FLAGS_enable_mkldnn, char_list_file,
+                     char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
-                       FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
+                     FLAGS_rec_batch_num);
  std::vector<cv::Mat> img_list;
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
@@ -154,7 +146,8 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
-        std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << endl;
      exit(1);
    }
    img_list.push_back(srcimg);
@@ -166,39 +159,31 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
  time_info[2] += rec_times[2];
  if (FLAGS_benchmark) {
-        AutoLogger autolog("ocr_rec", 
+    AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
-                           FLAGS_use_gpu,
+                       FLAGS_enable_mkldnn, FLAGS_cpu_threads,
-                           FLAGS_use_tensorrt,
+                       FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
-                           FLAGS_enable_mkldnn,
+                       time_info, cv_all_img_names.size());
-                           FLAGS_cpu_threads,
-                           FLAGS_rec_batch_num, 
-                           "dynamic", 
-                           FLAGS_precision, 
-                           time_info, 
-                           cv_all_img_names.size());
    autolog.report();
  }
  return 0;
 }
 int main_system(std::vector<cv::String> cv_all_img_names) {
  std::vector<double> time_info_det = {0, 0, 0};
  std::vector<double> time_info_rec = {0, 0, 0};
  DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                   FLAGS_gpu_mem, FLAGS_cpu_threads, 
+                 FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                   FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
+                 FLAGS_max_side_len, FLAGS_det_db_thresh,
                 FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
-                   FLAGS_use_polygon_score, FLAGS_visualize,
+                 FLAGS_use_polygon_score, FLAGS_use_dilation, FLAGS_visualize,
                 FLAGS_use_tensorrt, FLAGS_precision);
  Classifier *cls = nullptr;
  if (FLAGS_use_angle_cls) {
    cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                           FLAGS_gpu_mem, FLAGS_cpu_threads,
+                         FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                           FLAGS_enable_mkldnn, FLAGS_cls_thresh,
+                         FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
-                           FLAGS_use_tensorrt, FLAGS_precision);
  }
  std::string char_list_file = FLAGS_char_list_file;
@@ -207,16 +192,17 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  cout << "label file: " << char_list_file << endl;
  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                       FLAGS_gpu_mem, FLAGS_cpu_threads,
+                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                       FLAGS_enable_mkldnn, char_list_file,
+                     char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
-                       FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
+                     FLAGS_rec_batch_num);
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
    LOG(INFO) << "The predict img: " << cv_all_img_names[i];
    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
    if (!srcimg.data) {
-        std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << endl;
      exit(1);
    }
    std::vector<std::vector<std::vector<int>>> boxes;
@@ -245,26 +231,14 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  }
  if (FLAGS_benchmark) {
-        AutoLogger autolog_det("ocr_det", 
+    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
-                            FLAGS_use_gpu,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
-                            FLAGS_use_tensorrt,
+                           FLAGS_precision, time_info_det,
-                            FLAGS_enable_mkldnn,
-                            FLAGS_cpu_threads,
-                            1, 
-                            "dynamic", 
-                            FLAGS_precision, 
-                            time_info_det, 
-                            cv_all_img_names.size());
-        AutoLogger autolog_rec("ocr_rec", 
-                            FLAGS_use_gpu,
-                            FLAGS_use_tensorrt,
-                            FLAGS_enable_mkldnn,
-                            FLAGS_cpu_threads,
-                            FLAGS_rec_batch_num, 
-                            "dynamic", 
-                            FLAGS_precision, 
-                            time_info_rec, 
                           cv_all_img_names.size());
+    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
+                           time_info_rec, cv_all_img_names.size());
    autolog_det.report();
    std::cout << endl;
    autolog_rec.report();
@@ -272,29 +246,33 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  return 0;
 }
+void check_params(char *mode) {
-void check_params(char* mode) {
+  if (strcmp(mode, "det") == 0) {
-    if (strcmp(mode, "det")==0) {
    if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
-            std::cout << "Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
+      std::cout << "Usage[det]: ./ppocr "
+                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      exit(1);
    }
  }
-    if (strcmp(mode, "rec")==0) {
+  if (strcmp(mode, "rec") == 0) {
    if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
-            std::cout << "Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
+      std::cout << "Usage[rec]: ./ppocr "
+                   "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
      exit(1);
    }
  }
-    if (strcmp(mode, "system")==0) {
+  if (strcmp(mode, "system") == 0) {
-        if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) ||
+    if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() ||
+         FLAGS_image_dir.empty()) ||
        (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
-            std::cout << "Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
+      std::cout << "Usage[system without angle cls]: ./ppocr "
+                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
-            std::cout << "Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
+      std::cout << "Usage[system with angle cls]: ./ppocr "
+                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
                << "--use_angle_cls=true "
                << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
@@ -302,15 +280,17 @@ void check_params(char* mode) {
      exit(1);
    }
  }
-    if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && FLAGS_precision != "int8") {
+  if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
+      FLAGS_precision != "int8") {
    cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
    exit(1);
  }
 }
 int main(int argc, char **argv) {
-    if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) {
+  if (argc <= 1 ||
+      (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 &&
+       strcmp(argv[1], "system") != 0)) {
    std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
    return -1;
  }
@@ -321,7 +301,8 @@ int main(int argc, char **argv) {
  check_params(argv[1]);
  if (!PathExists(FLAGS_image_dir)) {
-        std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl;
+    std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
+              << endl;
    exit(1);
  }
@@ -329,14 +310,13 @@ int main(int argc, char **argv) {
  cv::glob(FLAGS_image_dir, cv_all_img_names);
  std::cout << "total images num: " << cv_all_img_names.size() << endl;
-    if (strcmp(argv[1], "det")==0) {
+  if (strcmp(argv[1], "det") == 0) {
    return main_det(cv_all_img_names);
  }
-    if (strcmp(argv[1], "rec")==0) {
+  if (strcmp(argv[1], "rec") == 0) {
    return main_rec(cv_all_img_names);
  }
-    if (strcmp(argv[1], "system")==0) {
+  if (strcmp(argv[1], "system") == 0) {
    return main_system(cv_all_img_names);
  }
 }
--- a/deploy/cpp_infer/src/ocr_det.cpp
+++ b/deploy/cpp_infer/src/ocr_det.cpp
@@ -14,7 +14,6 @@
 #include <include/ocr_det.h>
 namespace PaddleOCR {
 void DBDetector::LoadModel(const std::string &model_dir) {
@@ -33,10 +32,7 @@ void DBDetector::LoadModel(const std::string &model_dir) {
      if (this->precision_ == "int8") {
        precision = paddle_infer::Config::Precision::kInt8;
      }
-      config.EnableTensorRtEngine(
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
-          1 << 20, 10, 3,
-          precision,
-          false, false);
      std::map<std::string, std::vector<int>> min_input_shape = {
          {"x", {1, 3, 50, 50}},
          {"conv2d_92.tmp_0", {1, 96, 20, 20}},
@@ -157,22 +153,27 @@ void DBDetector::Run(cv::Mat &img,
  const double maxvalue = 255;
  cv::Mat bit_map;
  cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
-  cv::Mat dilation_map;
+  if (this->use_dilation_) {
-  cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
+    cv::Mat dila_ele =
-  cv::dilate(bit_map, dilation_map, dila_ele);
+        cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
+    cv::dilate(bit_map, bit_map, dila_ele);
+  }
  boxes = post_processor_.BoxesFromBitmap(
-      pred_map, dilation_map, this->det_db_box_thresh_,
+      pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
-      this->det_db_unclip_ratio_, this->use_polygon_score_);
+      this->use_polygon_score_);
  boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
  auto postprocess_end = std::chrono::steady_clock::now();
  std::cout << "Detected boxes num: " << boxes.size() << endl;
-  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
  times->push_back(double(preprocess_diff.count() * 1000));
  std::chrono::duration<float> inference_diff = inference_end - inference_start;
  times->push_back(double(inference_diff.count() * 1000));
-  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
  times->push_back(double(postprocess_diff.count() * 1000));
  //// visualization

--- a/deploy/cpp_infer/src/ocr_rec.cpp
+++ b/deploy/cpp_infer/src/ocr_rec.cpp
@@ -16,10 +16,14 @@
 namespace PaddleOCR {
-void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
-    std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+                         std::vector<double> *times) {
-    std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> preprocess_diff =
-    std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
  int img_num = img_list.size();
  std::vector<float> width_list;
@@ -28,27 +32,31 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *tim
  }
  std::vector<int> indices = Utility::argsort(width_list);
-    for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
+  for (int beg_img_no = 0; beg_img_no < img_num;
+       beg_img_no += this->rec_batch_num_) {
    auto preprocess_start = std::chrono::steady_clock::now();
    int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
    float max_wh_ratio = 0;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
      int h = img_list[indices[ino]].rows;
      int w = img_list[indices[ino]].cols;
      float wh_ratio = w * 1.0 / h;
      max_wh_ratio = max(max_wh_ratio, wh_ratio);
    }
+    int batch_width = 0;
    std::vector<cv::Mat> norm_img_batch;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
      cv::Mat srcimg;
      img_list[indices[ino]].copyTo(srcimg);
      cv::Mat resize_img;
-            this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
+      this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
-            this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
+                           this->use_tensorrt_);
+      this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                              this->is_scale_);
      norm_img_batch.push_back(resize_img);
+      batch_width = max(resize_img.cols, batch_width);
    }
-        int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
    std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
    this->permute_op_.Run(norm_img_batch, input.data());
    auto preprocess_end = std::chrono::steady_clock::now();
@@ -86,11 +94,11 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *tim
      float max_value = 0.0f;
      for (int n = 0; n < predict_shape[1]; n++) {
-                argmax_idx =
+        argmax_idx = int(Utility::argmax(
-                    int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
-                max_value =
+        max_value = float(*std::max_element(
-                    float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
        if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
@@ -116,7 +124,6 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *tim
  times->push_back(double(postprocess_diff.count() * 1000));
 }
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
  //   AnalysisConfig config;
  paddle_infer::Config config;
@@ -133,20 +140,14 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
      if (this->precision_ == "int8") {
        precision = paddle_infer::Config::Precision::kInt8;
      }
-      config.EnableTensorRtEngine(
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
-          1 << 20, 10, 3,
-          precision,
-          false, false);
      std::map<std::string, std::vector<int>> min_input_shape = {
-          {"x", {1, 3, 32, 10}},
+          {"x", {1, 3, 32, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}};
-          {"lstm_0.tmp_0", {10, 1, 96}}};
      std::map<std::string, std::vector<int>> max_input_shape = {
-          {"x", {1, 3, 32, 2000}},
+          {"x", {1, 3, 32, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}};
-          {"lstm_0.tmp_0", {1000, 1, 96}}};
      std::map<std::string, std::vector<int>> opt_input_shape = {
-          {"x", {1, 3, 32, 320}},
+          {"x", {1, 3, 32, 320}}, {"lstm_0.tmp_0", {25, 1, 96}}};
-          {"lstm_0.tmp_0", {25, 1, 96}}};
      config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                    opt_input_shape);
@@ -168,7 +169,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
  config.SwitchIrOptim(true);
  config.EnableMemoryOptim();
-//   config.DisableGlogInfo();
+  //   config.DisableGlogInfo();
  this->predictor_ = CreatePredictor(config);
 }

--- a/deploy/lite/readme.md
+++ b/deploy/lite/readme.md
+- [端侧部署](#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)
+    - [2.1 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)
 # 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。
@@ -26,17 +37,17 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
      | 平台 | 预测库下载链接 |
      |---|---|
-      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
-      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
+      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
-      注：1. 上述预测库为PaddleLite 2.9分支编译得到，有关PaddleLite 2.9 详细信息可参考 [链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.9) 。
+      注：1. 上述预测库为PaddleLite 2.10分支编译得到，有关PaddleLite 2.10 详细信息可参考 [链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.10) 。
 - 2. [推荐]编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# 切换到Paddle-Lite release/v2.9 稳定分支
+# 切换到Paddle-Lite release/v2.10 稳定分支
-git checkout release/v2.9
+git checkout release/v2.10
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
@@ -85,8 +96,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
 |---|---|---|---|---|---|---|
-|V2.0|超轻量中文OCR 移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
-|V2.0(slim)|超轻量中文OCR 移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 如果直接使用上述表格中的模型进行部署，可略过下述步骤，直接阅读 [2.2节](#2.2与手机联调)。
@@ -97,7 +108,7 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 # 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-git checkout release/v2.9
+git checkout release/v2.10
 # 启动编译
 ./lite/tools/build.sh build_optimize_tool
 ```
@@ -123,15 +134,15 @@ cd build.opt/lite/api/
 下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。
 ```
-# 【推荐】 下载PaddleOCR V2.0版本的中英文 inference模型
+# 【推荐】 下载 PP-OCRv2版本的中英文 inference模型
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_det_slim_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_det_slim_quant_infer.tar
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_rec_slim_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_rec_slim_quant_infer.tar
 wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_cls_slim_infer.tar
-# 转换V2.0检测模型
+# 转换检测模型
-./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# 转换V2.0识别模型
+# 转换识别模型
-./opt --model_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# 转换V2.0方向分类器模型
+# 转换方向分类器模型
 ./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
 ```
@@ -186,15 +197,15 @@ wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls
 ```
 准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
- 准备lite opt工具优化后的模型文件，比如使用`ch_ppocr_mobile_v2.0_det_slim_opt.nb，ch_ppocr_mobile_v2.0_rec_slim_opt.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
+ 准备lite opt工具优化后的模型文件，比如使用`ch_PP-OCRv2_det_slim_opt.ch_PP-OCRv2_rec_slim_rec.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
 执行完成后，ocr文件夹下将有如下文件格式：
 ```
 demo/cxx/ocr/
 |-- debug/  
-|   |--ch_ppocr_mobile_v2.0_det_slim_opt.nb           优化后的检测模型文件
+|   |--ch_PP-OCRv2_det_slim_opt.nb           优化后的检测模型文件
-|   |--ch_ppocr_mobile_v2.0_rec_slim_opt.nb           优化后的识别模型文件
+|   |--ch_PP-OCRv2_rec_slim_opt.nb           优化后的识别模型文件
 |   |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb           优化后的文字方向分类器模型文件
 |   |--11.jpg                           待测试图像
 |   |--ppocr_keys_v1.txt                中文字典文件
@@ -250,7 +261,7 @@ use_direction_classify  0  # 是否使用方向分类器，0表示不使用，1
 export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
 # 开始使用，ocr_db_crnn可执行文件的使用方式为:
 # ./ocr_db_crnn  检测模型文件 方向分类器模型文件  识别模型文件  测试图像路径  字典文件路径
- ./ocr_db_crnn ch_ppocr_mobile_v2.0_det_slim_opt.nb  ch_ppocr_mobile_v2.0_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_slim_opt.nb  ./11.jpg  ppocr_keys_v1.txt
+ ./ocr_db_crnn ch_PP-OCRv2_det_slim_opt.nb  ch_PP-OCRv2_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_slim_opt.nb  ./11.jpg  ppocr_keys_v1.txt
 ```
 如果对代码做了修改，则需要重新编译并push到手机上。

--- a/deploy/lite/readme_en.md
+++ b/deploy/lite/readme_en.md
+- [Tutorial of PaddleOCR Mobile deployment](#tutorial-of-paddleocr-mobile-deployment)
+  - [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)
+    - [2.1 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)
 # 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.
@@ -28,17 +39,17 @@ There are two ways to obtain the Paddle-Lite library：
      | Platform | Paddle-Lite library download link |
      |---|---|
-      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
-      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
+      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
-      Note: 1. The above Paddle-Lite library is compiled from the Paddle-Lite 2.9 branch. For more information about Paddle-Lite 2.9, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.9).
+      Note: 1. The above Paddle-Lite library is compiled from the Paddle-Lite 2.10 branch. For more information about Paddle-Lite 2.10, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.10).
 - 2. [Recommended] Compile Paddle-Lite to get the prediction library. The compilation method of Paddle-Lite is as follows：
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# Switch to Paddle-Lite release/v2.8 stable branch
+# Switch to Paddle-Lite release/v2.10 stable branch
-git checkout release/v2.8
+git checkout release/v2.10
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
@@ -87,10 +98,10 @@ The following table also provides a series of models that can be deployed on mob
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
-|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
+|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
-|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
-If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2 Run optimized model on Phone).
+If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2-Run-optimized-model-on-Phone).
 If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model.
@@ -98,7 +109,7 @@ The `opt` tool can be obtained by compiling Paddle Lite.
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-git checkout release/v2.9
+git checkout release/v2.10
 ./lite/tools/build.sh build_optimize_tool
 ```
@@ -124,22 +135,22 @@ cd build.opt/lite/api/
 The following takes the ultra-lightweight Chinese model of PaddleOCR as an example to introduce the use of the compiled opt file to complete the conversion of the inference model to the Paddle-Lite optimized model
 ```
-# [Recommendation] Download the Chinese and English inference model of PaddleOCR V2.0
+# 【[Recommendation] Download the Chinese and English inference model of PP-OCRv2
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_det_slim_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_det_slim_quant_infer.tar
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_rec_slim_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_rec_slim_quant_infer.tar
 wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_cls_slim_infer.tar
-# Convert V2.0 detection model
+# Convert detection model
-./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# Convert V2.0 recognition model
+# Convert recognition model
-./opt --model_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# Convert V2.0 angle classifier model
+# Convert angle classifier model
 ./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
 ```
 After the conversion is successful, there will be more files ending with `.nb` in the inference model directory, which is the successfully converted model file.
-<a name="2.2 Run optimized model on Phone"></a>
+<a name="2.2-Run-optimized-model-on-Phone"></a>
 ### 2.2 Run optimized model on Phone
 Some preparatory work is required first.
@@ -194,8 +205,8 @@ The structure of the OCR demo is as follows after the above command is executed:
 ```
 demo/cxx/ocr/
 |-- debug/  
-|   |--ch_ppocr_mobile_v2.0_det_slim_opt.nb           Detection model
+|   |--ch_PP-OCRv2_det_slim_opt.nb           Detection model
-|   |--ch_ppocr_mobile_v2.0_rec_slim_opt.nb           Recognition model
+|   |--ch_PP-OCRv2_rec_slim_opt.nb           Recognition model
 |   |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb           Text direction classification model
 |   |--11.jpg                           Image for OCR
 |   |--ppocr_keys_v1.txt                Dictionary file
@@ -249,7 +260,7 @@ After the above steps are completed, you can use adb to push the file to the pho
 export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
 # The use of ocr_db_crnn is:
 # ./ocr_db_crnn Detection model file Orientation classifier model file Recognition model file Test image path Dictionary file path
- ./ocr_db_crnn ch_ppocr_mobile_v2.0_det_opt.nb  ch_ppocr_mobile_v2.0_rec_opt.nb  ch_ppocr_mobile_v2.0_cls_opt.nb  ./11.jpg  ppocr_keys_v1.txt
+ ./ocr_db_crnn ch_PP-OCRv2_det_slim_opt.nb  ch_PP-OCRv2_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_opt.nb  ./11.jpg  ppocr_keys_v1.txt
 ```
 If you modify the code, you need to recompile and push to the phone.

--- a/deploy/paddle2onnx/images/lite_demo_onnx.png
+++ b/deploy/paddle2onnx/images/lite_demo_onnx.png
--- a/deploy/paddle2onnx/images/lite_demo_paddle.png
+++ b/deploy/paddle2onnx/images/lite_demo_paddle.png
--- a/deploy/paddle2onnx/readme.md
+++ b/deploy/paddle2onnx/readme.md
 # paddle2onnx 模型转化与预测
-本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNX 引擎预测。
+本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNXRuntime 引擎预测。
 ## 1. 环境准备
-需要准备 Paddle2ONNX 模型转化环境，和 ONNX 模型预测环境
+需要准备 PaddleOCR、Paddle2ONNX 模型转化环境，和 ONNXRuntime 预测环境
+###  PaddleOCR
+克隆PaddleOCR的仓库，使用release/2.4分支，并进行安装，由于PaddleOCR仓库比较大，git clone速度比较慢，所以本教程已下载
+```
+git clone  -b release/2.4 https://github.com/PaddlePaddle/PaddleOCR.git
+cd PaddleOCR && python3.7 setup.py install
+```
 ###  Paddle2ONNX
@@ -16,7 +25,7 @@ Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式，算
 python3.7 -m pip install paddle2onnx
 ```
- 安装 ONNX
+- 安装 ONNXRuntime
 ```
 # 建议安装 1.9.0 版本，可根据环境更换版本号
 python3.7 -m pip install onnxruntime==1.9.0
@@ -30,11 +39,17 @@ 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 检测模型为例：
+以 ppocr 中文检测、识别、分类模型为例：
 ```
-wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
-cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ..
+cd ./inference && tar xf ch_PP-OCRv2_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/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 ..
 ```
 - 模型转换
@@ -42,35 +57,160 @@ cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_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_ppocr_mobile_v2.0_det_infer/ \
+paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
--model_filename=inference.pdmodel \
+--model_filename inference.pdmodel \
--params_filename=inference.pdiparams \
+--params_filename inference.pdiparams \
--save_file=./inference/det_mobile_onnx/model.onnx \
+--save_file ./inference/det_onnx/model.onnx \
--opset_version=10 \
+--opset_version 10 \
--input_shape_dict="{'x': [-1, 3, -1, -1]}" \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
--enable_onnx_checker=True
+--enable_onnx_checker True
+paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
+--model_filename inference.pdmodel \
+--params_filename inference.pdiparams \
+--save_file ./inference/rec_onnx/model.onnx \
+--opset_version 10 \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
+--enable_onnx_checker True
+paddle2onnx --model_dir ./inference/ch_ppocr_mobile_v2.0_cls_infer \
+--model_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel \
+--params_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams \
+--save_file ./inferencecls_onnx/model.onnx \
+--opset_version 10 \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
+--enable_onnx_checker True
 ```
-执行完毕后，ONNX 模型会被保存在 `./inference/det_mobile_onnx/` 路径下
+执行完毕后，ONNX 模型会被分别保存在 `./inference/det_onnx/`，`./inference/rec_onnx/`，`./inference/cls_onnx/`路径下
 * 注意：对于OCR模型，转化过程中必须采用动态shape的形式，即加入选项--input_shape_dict="{'x': [-1, 3, -1, -1]}"，否则预测结果可能与直接使用Paddle预测有细微不同。
  另外，以下几个模型暂不支持转换为 ONNX 模型：
  NRTR、SAR、RARE、SRN
-## 3. onnx 预测
+## 3. 推理预测
+以中文OCR模型为例，使用 ONNXRuntime 预测可执行如下命令：
+```
+python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
+--det_model_dir=./inference/det_onnx/model.onnx  \
+--rec_model_dir=./inference/rec_onnx/model.onnx  \
+--cls_model_dir=./inference/cls_onnx/model.onnx  \
+--image_dir=./deploy/lite/imgs/lite_demo.png
+```
+以中文OCR模型为例，使用 Paddle Inference 预测可执行如下命令：
+```
+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 \
+--image_dir=./deploy/lite/imgs/lite_demo.png
+```
+执行命令后在终端会打印出预测的识别信息，并在 `./inference_results/` 下保存可视化结果。
+ONNXRuntime 执行效果：
+<div align="center">
+    <img src="./images/lite_demo_onnx.png" width=800">
+</div>
+Paddle Inference 执行效果：
+<div align="center">
+    <img src="./images/lite_demo_paddle.png" width=800">
+</div>
-以检测模型为例，使用 ONNX 预测可执行如下命令：
+使用 ONNXRuntime 预测，终端输出：
 ```
-python3.7 ../../tools/infer/predict_det.py --use_gpu=False --use_onnx=True \
+[2022/02/22 17:48:27] root DEBUG: dt_boxes num : 38, elapse : 0.043187856674194336
--det_model_dir=./inference/det_mobile_onnx/model.onnx \
+[2022/02/22 17:48:27] root DEBUG: rec_res num  : 38, elapse : 0.592170000076294
--image_dir=../../doc/imgs/1.jpg
+[2022/02/22 17:48:27] root DEBUG: 0  Predict time of ./deploy/lite/imgs/lite_demo.png: 0.642s
+[2022/02/22 17:48:27] root DEBUG: The, 0.984
+[2022/02/22 17:48:27] root DEBUG: visualized, 0.882
+[2022/02/22 17:48:27] root DEBUG: etect18片, 0.720
+[2022/02/22 17:48:27] root DEBUG: image saved in./vis.jpg, 0.947
+[2022/02/22 17:48:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
+[2022/02/22 17:48:27] root DEBUG: 产品信息/参数, 0.922
+[2022/02/22 17:48:27] root DEBUG: 0.992728, 0.914
+[2022/02/22 17:48:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
+[2022/02/22 17:48:27] root DEBUG: 0.97417, 0.977
+[2022/02/22 17:48:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
+[2022/02/22 17:48:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
+[2022/02/22 17:48:27] root DEBUG: 0.985133, 0.980
+[2022/02/22 17:48:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
+[2022/02/22 17:48:27] root DEBUG: 0.995007, 0.883
+[2022/02/22 17:48:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
+[2022/02/22 17:48:27] root DEBUG: 【净含量】：220ml, 0.943
+[2022/02/22 17:48:27] root DEBUG: Q.996577, 0.932
+[2022/02/22 17:48:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
+[2022/02/22 17:48:27] root DEBUG: 0.995842, 0.969
+[2022/02/22 17:48:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
+[2022/02/22 17:48:27] root DEBUG: 0.961928, 0.964
+[2022/02/22 17:48:27] root DEBUG: 10, 0.812
+[2022/02/22 17:48:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
+[2022/02/22 17:48:27] root DEBUG: 0.925898, 0.943
+[2022/02/22 17:48:27] root DEBUG: （成品包材）, 0.974
+[2022/02/22 17:48:27] root DEBUG: 0.972573, 0.961
+[2022/02/22 17:48:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
+[2022/02/22 17:48:27] root DEBUG: 0.994448, 0.952
+[2022/02/22 17:48:27] root DEBUG: 13, 0.998
+[2022/02/22 17:48:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
+[2022/02/22 17:48:27] root DEBUG: 0.990198, 0.975
+[2022/02/22 17:48:27] root DEBUG: 14, 0.977
+[2022/02/22 17:48:27] root DEBUG: 发足够的滋养, 0.991
+[2022/02/22 17:48:27] root DEBUG: 0.997668, 0.918
+[2022/02/22 17:48:27] root DEBUG: 花费了0.457335秒, 0.901
+[2022/02/22 17:48:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
+[2022/02/22 17:48:27] root INFO: The predict total time is 0.7003889083862305
 ```
-执行命令后在终端会打印出预测的检测框坐标，并在 `./inference_results/` 下保存可视化结果。
+使用 Paddle Inference 预测，终端输出：
 ```
-root INFO: 1.jpg  [[[291, 295], [334, 292], [348, 844], [305, 847]], [[344, 296], [379, 294], [387, 669], [353, 671]]]
+[2022/02/22 17:47:25] root DEBUG: dt_boxes num : 38, elapse : 0.11791276931762695
-The predict time of ../../doc/imgs/1.jpg: 0.06162881851196289
+[2022/02/22 17:47:27] root DEBUG: rec_res num  : 38, elapse : 2.6206860542297363
-The visualized image saved in ./inference_results/det_res_1.jpg
+[2022/02/22 17:47:27] root DEBUG: 0  Predict time of ./deploy/lite/imgs/lite_demo.png: 2.746s
+[2022/02/22 17:47:27] root DEBUG: The, 0.984
+[2022/02/22 17:47:27] root DEBUG: visualized, 0.882
+[2022/02/22 17:47:27] root DEBUG: etect18片, 0.720
+[2022/02/22 17:47:27] root DEBUG: image saved in./vis.jpg, 0.947
+[2022/02/22 17:47:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
+[2022/02/22 17:47:27] root DEBUG: 产品信息/参数, 0.922
+[2022/02/22 17:47:27] root DEBUG: 0.992728, 0.914
+[2022/02/22 17:47:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
+[2022/02/22 17:47:27] root DEBUG: 0.97417, 0.977
+[2022/02/22 17:47:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
+[2022/02/22 17:47:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
+[2022/02/22 17:47:27] root DEBUG: 0.985133, 0.980
+[2022/02/22 17:47:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
+[2022/02/22 17:47:27] root DEBUG: 0.995007, 0.883
+[2022/02/22 17:47:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
+[2022/02/22 17:47:27] root DEBUG: 【净含量】：220ml, 0.943
+[2022/02/22 17:47:27] root DEBUG: Q.996577, 0.932
+[2022/02/22 17:47:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
+[2022/02/22 17:47:27] root DEBUG: 0.995842, 0.969
+[2022/02/22 17:47:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
+[2022/02/22 17:47:27] root DEBUG: 0.961928, 0.964
+[2022/02/22 17:47:27] root DEBUG: 10, 0.812
+[2022/02/22 17:47:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
+[2022/02/22 17:47:27] root DEBUG: 0.925898, 0.943
+[2022/02/22 17:47:27] root DEBUG: （成品包材）, 0.974
+[2022/02/22 17:47:27] root DEBUG: 0.972573, 0.961
+[2022/02/22 17:47:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
+[2022/02/22 17:47:27] root DEBUG: 0.994448, 0.952
+[2022/02/22 17:47:27] root DEBUG: 13, 0.998
+[2022/02/22 17:47:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
+[2022/02/22 17:47:27] root DEBUG: 0.990198, 0.975
+[2022/02/22 17:47:27] root DEBUG: 14, 0.977
+[2022/02/22 17:47:27] root DEBUG: 发足够的滋养, 0.991
+[2022/02/22 17:47:27] root DEBUG: 0.997668, 0.918
+[2022/02/22 17:47:27] root DEBUG: 花费了0.457335秒, 0.901
+[2022/02/22 17:47:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
+[2022/02/22 17:47:27] root INFO: The predict total time is 2.8338775634765625
 ```
--- a/deploy/pdserving/README.md
+++ b/deploy/pdserving/README.md
@@ -34,35 +34,26 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
 PaddleOCR operating environment and Paddle Serving operating environment are needed.
 1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
-   Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.
+   Download the corresponding paddlepaddle whl package according to the environment, it is recommended to install version 2.2.2.
 2. The steps of PaddleServing operating environment prepare are as follows:
-    Install serving which used to start the service
-    ```
-    pip3 install paddle-serving-server==0.6.1 # for CPU
-    pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
-    # Other GPU environments need to confirm the environment and then choose to execute the following commands
-    pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
-    pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
-    ```
-3. Install the client to send requests to the service
 ```bash
-# 安装serving，用于启动服务
+# 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
-# 如果是cuda10.1环境，可以使用下面的命令安装paddle-serving-server
+# 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
-# 安装client，用于向服务发送请求
+# 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
-# 安装serving-app
+# 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
 ```
@@ -87,27 +78,27 @@ Then, you can use installed paddle_serving_client tool to convert inference mode
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
                                         --model_filename inference.pdmodel          \
                                         --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_det_serving/ \
+                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
-                                         --serving_client ./ppocrv2_det_client/
+                                         --serving_client ./ppocr_det_mobile_2.0_client/
 #  Recognition model conversion
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
                                         --model_filename inference.pdmodel          \
                                         --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_rec_serving/  \
+                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
-                                         --serving_client ./ppocrv2_rec_client/
+                                         --serving_client ./ppocr_rec_mobile_2.0_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:
 ```
-|- ppocrv2_det_serving/
+|- ppocr_det_mobile_2.0_serving/
  |- __model__  
  |- __params__
  |- serving_server_conf.prototxt  
  |- serving_server_conf.stream.prototxt
-|- ppocrv2_det_client
+|- ppocr_det_mobile_2.0_client
  |- serving_client_conf.prototxt  
  |- serving_client_conf.stream.prototxt

--- a/deploy/pdserving/README_CN.md
+++ b/deploy/pdserving/README_CN.md
@@ -31,7 +31,7 @@ PaddleOCR提供2种服务部署方式：
 需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
 - 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
-  根据环境下载对应的paddle whl包，推荐安装2.0.1版本
+  根据环境下载对应的paddlepaddle whl包，推荐安装2.2.2版本
 - 准备PaddleServing的运行环境，步骤如下
@@ -75,26 +75,26 @@ wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
                                         --model_filename inference.pdmodel          \
                                         --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_det_serving/ \
+                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
-                                         --serving_client ./ppocrv2_det_client/
+                                         --serving_client ./ppocr_det_mobile_2.0_client/
 # 转换识别模型
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
                                         --model_filename inference.pdmodel          \
                                         --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_rec_serving/  \
+                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
-                                         --serving_client ./ppocrv2_rec_client/
+                                         --serving_client ./ppocr_rec_mobile_2.0_client/
 ```
-检测模型转换完成后，会在当前文件夹多出`ppocrv2_det_serving` 和`ppocrv2_det_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_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹，具备如下格式：
 ```
-|- ppocrv2_det_serving/
+|- ppocr_det_mobile_2.0_serving/
  |- __model__  
  |- __params__
  |- serving_server_conf.prototxt  
  |- serving_server_conf.stream.prototxt
-|- ppocrv2_det_client
+|- ppocr_det_mobile_2.0_client
  |- serving_client_conf.prototxt  
  |- serving_client_conf.stream.prototxt

--- a/deploy/pdserving/config.yml
+++ b/deploy/pdserving/config.yml
@@ -34,7 +34,7 @@ op:
            client_type: local_predictor
            #det模型路径
-            model_config: ./ppocrv2_det_serving
+            model_config: ./ppocr_det_mobile_2.0_serving
            #Fetch结果列表，以client_config中fetch_var的alias_name为准
            fetch_list: ["save_infer_model/scale_0.tmp_1"]
@@ -60,7 +60,7 @@ op:
            client_type: local_predictor
            #rec模型路径
-            model_config: ./ppocrv2_rec_serving
+            model_config: ./ppocr_rec_mobile_2.0_serving
            #Fetch结果列表，以client_config中fetch_var的alias_name为准
            fetch_list: ["save_infer_model/scale_0.tmp_1"]  

--- a/deploy/pdserving/ocr_reader.py
+++ b/deploy/pdserving/ocr_reader.py
@@ -433,3 +433,54 @@ class OCRReader(object):
        text = self.label_ops.decode(
            preds_idx, preds_prob, is_remove_duplicate=True)
        return text
+from argparse import ArgumentParser,RawDescriptionHelpFormatter
+import yaml
+class ArgsParser(ArgumentParser):
+    def __init__(self):
+        super(ArgsParser, self).__init__(
+            formatter_class=RawDescriptionHelpFormatter)
+        self.add_argument("-c", "--config", help="configuration file to use")
+        self.add_argument(
+            "-o", "--opt", nargs='+', help="set configuration options")
+    def parse_args(self, argv=None):
+        args = super(ArgsParser, self).parse_args(argv)
+        assert args.config is not None, \
+            "Please specify --config=configure_file_path."
+        args.conf_dict = self._parse_opt(args.opt, args.config)
+        print("args config:", args.conf_dict)
+        return args
+    def _parse_helper(self, v):
+        if v.isnumeric():
+            if "." in v:
+                v = float(v)
+            else:
+                v = int(v)
+        elif v == "True" or v == "False":
+            v = (v == "True")
+        return v
+    def _parse_opt(self, opts, conf_path):
+        f = open(conf_path)
+        config = yaml.load(f, Loader=yaml.Loader)
+        if not opts:
+            return config
+        for s in opts:
+            s = s.strip()
+            k, v = s.split('=')
+            v = self._parse_helper(v)
+            print(k,v, type(v))
+            cur = config
+            parent = cur
+            for kk in k.split("."):
+                if kk not in cur:
+                     cur[kk] = {}
+                     parent = cur
+                     cur = cur[kk]
+                else:
+                     parent = cur
+                     cur = cur[kk]
+            parent[k.split(".")[-1]] = v
+        return config
\ No newline at end of file
--- a/deploy/pdserving/web_service_det.py
+++ b/deploy/pdserving/web_service_det.py
@@ -18,7 +18,7 @@ import numpy as np
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
-from ocr_reader import OCRReader, DetResizeForTest
+from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
 from paddle_serving_app.reader import Sequential, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
@@ -73,5 +73,6 @@ class OcrService(WebService):
 uci_service = OcrService(name="ocr")
-uci_service.prepare_pipeline_config("config.yml")
+FLAGS = ArgsParser().parse_args()
+uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
 uci_service.run_service()
--- a/deploy/pdserving/web_service_rec.py
+++ b/deploy/pdserving/web_service_rec.py
@@ -18,7 +18,7 @@ import numpy as np
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
-from ocr_reader import OCRReader, DetResizeForTest
+from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
 from paddle_serving_app.reader import Sequential, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
@@ -82,5 +82,6 @@ class OcrService(WebService):
 uci_service = OcrService(name="ocr")
-uci_service.prepare_pipeline_config("config.yml")
+FLAGS = ArgsParser().parse_args()
+uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
 uci_service.run_service()
--- a/doc/doc_ch/FAQ.md
+++ b/doc/doc_ch/FAQ.md
@@ -349,7 +349,7 @@ A：PaddleOCR已完成Windows和Mac系统适配，运行时注意两点：
 #### Q：训练文字识别模型，真实数据有30w，合成数据有500w，需要做样本均衡吗？
-A：需要，一般需要保证一个batch中真实数据样本和合成数据样本的比例是1：1~1：3左右效果比较理想。如果合成数据过大，会过拟合到合成数据，预测效果往往不佳。还有一种启发性的尝试是可以先用大量合成数据训练一个base模型，然后再用真实数据微调，在一些简单场景效果也是会有提升的。
+A：需要，一般需要保证一个batch中真实数据样本和合成数据样本的比例是5：1~10：1左右效果比较理想。如果合成数据过大，会过拟合到合成数据，预测效果往往不佳。还有一种启发性的尝试是可以先用大量合成数据训练一个base模型，然后再用真实数据微调，在一些简单场景效果也是会有提升的。
 #### Q: 当训练数据量少时，如何获取更多的数据？

--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
 # 两阶段算法
- [两阶段算法](#-----)
+- [两阶段算法](#两阶段算法)
-  * [1. 算法介绍](#1)
+  - [1. 算法介绍](#1-算法介绍)
-    + [1.1 文本检测算法](#11)
+    - [1.1 文本检测算法](#11-文本检测算法)
-    + [1.2 文本识别算法](#12)
+    - [1.2 文本识别算法](#12-文本识别算法)
-  * [2. 模型训练](#2)
+  - [2. 模型训练](#2-模型训练)
-  * [3. 模型推理](#3)
+  - [3. 模型推理](#3-模型推理)
 <a name="1"></a>
@@ -21,6 +21,7 @@ PaddleOCR开源的文本检测算法列表：
 - [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
 - [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
 - [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
+- [x]  FCENet([paper](https://arxiv.org/abs/2104.10442))
 在ICDAR2015文本检测公开数据集上，算法效果如下：
 |模型|骨干网络|precision|recall|Hmean|下载链接|
@@ -39,6 +40,12 @@ PaddleOCR开源的文本检测算法列表：
 | --- | --- | --- | --- | --- | --- |
 |SAST|ResNet50_vd|89.63%|78.44%|83.66%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
+在CTW1500文本检测公开数据集上，算法效果如下：
+|模型|骨干网络|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- |         
+|FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
 **说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
 * [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
 * [Google Drive下载地址](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)

--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -10,6 +10,7 @@
  * [2.1 启动训练](#21-----)
  * [2.2 断点训练](#22-----)
  * [2.3 更换Backbone 训练](#23---backbone---)
+  * [2.4 知识蒸馏训练](#24---distill---)
 - [3. 模型评估与预测](#3--------)
  * [3.1 指标评估](#31-----)
  * [3.2 测试检测效果](#32-------)
@@ -182,6 +183,15 @@ args1: args1
 **注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
+<a name="24---distill---"></a>
+## 2.4 知识蒸馏训练
+PaddleOCR支持了基于知识蒸馏的检测模型训练过程，更多内容可以参考[知识蒸馏说明文档](./knowledge_distillation.md)。
 <a name="3--------"></a>
 # 3. 模型评估与预测

--- a/doc/doc_ch/models_list.md
+++ b/doc/doc_ch/models_list.md
@@ -6,13 +6,14 @@
 > 3. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
- [1. 文本检测模型](#文本检测模型)
+- [PP-OCR系列模型列表（V2.1，2021年9月6日更新）](#pp-ocr系列模型列表v212021年9月6日更新)
- [2. 文本识别模型](#文本识别模型)
+  - [1. 文本检测模型](#1-文本检测模型)
-    - [2.1 中文识别模型](#中文识别模型)
+  - [2. 文本识别模型](#2-文本识别模型)
-    - [2.2 英文识别模型](#英文识别模型)
+    - [2.1 中文识别模型](#21-中文识别模型)
-    - [2.3 多语言识别模型](#多语言识别模型)
+    - [2.2 英文识别模型](#22-英文识别模型)
- [3. 文本方向分类模型](#文本方向分类模型)
+    - [2.3 多语言识别模型（更多语言持续更新中...）](#23-多语言识别模型更多语言持续更新中)
- [4. Paddle-Lite 模型](#Paddle-Lite模型)
+  - [3. 文本方向分类模型](#3-文本方向分类模型)
+  - [4. Paddle-Lite 模型](#4-paddle-lite-模型)
 PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
@@ -100,6 +101,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
 |---|---|---|---|---|---|---|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 |PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
 |PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
 |V2.0|ppocr_v2.0超轻量中文OCR移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|

--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
 # PaddleOCR快速开始
+- [1. 安装PaddleOCR whl包](#1)
- [PaddleOCR快速开始](#paddleocr)
+- [2. 便捷使用](#2)
+  - [2.1 命令行使用](#21)
-  + [1. 安装PaddleOCR whl包](#1)
-  * [2. 便捷使用](#2)
-    + [2.1 命令行使用](#21)
      - [2.1.1 中英文模型](#211)
      - [2.1.2 多语言模型](#212)
      - [2.1.3 版面分析](#213)
-    + [2.2 Python脚本使用](#22)
+  - [2.2 Python脚本使用](#22)
      - [2.2.1 中英文与多语言使用](#221)
      - [2.2.2 版面分析](#222)