Merge pull request #7648 from WenmuZhou/cpp_infer

add layout cpp infer
2026-09-24 23:33:08 +08:00 · 2022-09-20 15:09:51 +08:00
parent 0a59848d3a dd9f48da2e
commit 0663b03918
25 changed files with 1047 additions and 474 deletions
@@ -49,6 +49,11 @@ DECLARE_int32(rec_batch_num);
 DECLARE_string(rec_char_dict_path);
 DECLARE_int32(rec_img_h);
 DECLARE_int32(rec_img_w);
+// layout model related
+DECLARE_string(layout_model_dir);
+DECLARE_string(layout_dict_path);
+DECLARE_double(layout_score_threshold);
+DECLARE_double(layout_nms_threshold);
 // structure model related
 DECLARE_string(table_model_dir);
 DECLARE_int32(table_max_len);
@@ -59,4 +64,5 @@ DECLARE_bool(merge_no_span_structure);
 DECLARE_bool(det);
 DECLARE_bool(rec);
 DECLARE_bool(cls);
-DECLARE_bool(table);
+DECLARE_bool(table);
+DECLARE_bool(layout);
@@ -14,26 +14,12 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>

 #include <include/preprocess_op.h>
 #include <include/utility.h>

-using namespace paddle_infer;
-
 namespace PaddleOCR {

 class Classifier {
@@ -66,7 +52,7 @@ public:
           std::vector<float> &cls_scores, std::vector<double> &times);

 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;

  bool use_gpu_ = false;
  int gpu_id_ = 0;
@@ -14,26 +14,12 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>

 #include <include/postprocess_op.h>
 #include <include/preprocess_op.h>

-using namespace paddle_infer;
-
 namespace PaddleOCR {

 class DBDetector {
@@ -41,7 +27,7 @@ public:
  explicit DBDetector(const std::string &model_dir, const bool &use_gpu,
                      const int &gpu_id, const int &gpu_mem,
                      const int &cpu_math_library_num_threads,
-                      const bool &use_mkldnn, const string &limit_type,
+                      const bool &use_mkldnn, const std::string &limit_type,
                      const int &limit_side_len, const double &det_db_thresh,
                      const double &det_db_box_thresh,
                      const double &det_db_unclip_ratio,
@@ -77,7 +63,7 @@ public:
           std::vector<double> &times);

 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;

  bool use_gpu_ = false;
  int gpu_id_ = 0;
@@ -85,7 +71,7 @@ private:
  int cpu_math_library_num_threads_ = 4;
  bool use_mkldnn_ = false;

-  string limit_type_ = "max";
+  std::string limit_type_ = "max";
  int limit_side_len_ = 960;

  double det_db_thresh_ = 0.3;
@@ -14,27 +14,12 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>

 #include <include/ocr_cls.h>
-#include <include/preprocess_op.h>
 #include <include/utility.h>

-using namespace paddle_infer;
-
 namespace PaddleOCR {

 class CRNNRecognizer {
@@ -42,7 +27,7 @@ public:
  explicit CRNNRecognizer(const std::string &model_dir, const bool &use_gpu,
                          const int &gpu_id, const int &gpu_mem,
                          const int &cpu_math_library_num_threads,
-                          const bool &use_mkldnn, const string &label_path,
+                          const bool &use_mkldnn, const std::string &label_path,
                          const bool &use_tensorrt,
                          const std::string &precision,
                          const int &rec_batch_num, const int &rec_img_h,
@@ -75,7 +60,7 @@ public:
           std::vector<float> &rec_text_scores, std::vector<double> &times);

 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;

  bool use_gpu_ = false;
  int gpu_id_ = 0;
@@ -14,28 +14,9 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/ocr_cls.h>
 #include <include/ocr_det.h>
 #include <include/ocr_rec.h>
-#include <include/preprocess_op.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;

 namespace PaddleOCR {

@@ -43,21 +24,27 @@ class PPOCR {
 public:
  explicit PPOCR();
  ~PPOCR();
-  std::vector<std::vector<OCRPredictResult>>
-  ocr(std::vector<cv::String> cv_all_img_names, bool det = true,
-      bool rec = true, bool cls = true);
+
+  std::vector<std::vector<OCRPredictResult>> ocr(std::vector<cv::Mat> img_list,
+                                                 bool det = true,
+                                                 bool rec = true,
+                                                 bool cls = true);
+  std::vector<OCRPredictResult> ocr(cv::Mat img, bool det = true,
+                                    bool rec = true, bool cls = true);
+
+  void reset_timer();
+  void benchmark_log(int img_num);

 protected:
-  void det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
+  std::vector<double> time_info_det = {0, 0, 0};
+  std::vector<double> time_info_rec = {0, 0, 0};
+  std::vector<double> time_info_cls = {0, 0, 0};
+
+  void det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results);
  void rec(std::vector<cv::Mat> img_list,
-           std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
+           std::vector<OCRPredictResult> &ocr_results);
  void cls(std::vector<cv::Mat> img_list,
-           std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
-  void log(std::vector<double> &det_times, std::vector<double> &rec_times,
-           std::vector<double> &cls_times, int img_num);
+           std::vector<OCRPredictResult> &ocr_results);

 private:
  DBDetector *detector_ = nullptr;
@@ -14,27 +14,9 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/paddleocr.h>
-#include <include/preprocess_op.h>
+#include <include/structure_layout.h>
 #include <include/structure_table.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;

 namespace PaddleOCR {

@@ -42,23 +24,31 @@ class PaddleStructure : public PPOCR {
 public:
  explicit PaddleStructure();
  ~PaddleStructure();
-  std::vector<std::vector<StructurePredictResult>>
-  structure(std::vector<cv::String> cv_all_img_names, bool layout = false,
-            bool table = true);
+
+  std::vector<StructurePredictResult> structure(cv::Mat img,
+                                                bool layout = false,
+                                                bool table = true,
+                                                bool ocr = false);
+
+  void reset_timer();
+  void benchmark_log(int img_num);

 private:
-  StructureTableRecognizer *recognizer_ = nullptr;
+  std::vector<double> time_info_table = {0, 0, 0};
+  std::vector<double> time_info_layout = {0, 0, 0};
+
+  StructureTableRecognizer *table_model_ = nullptr;
+  StructureLayoutRecognizer *layout_model_ = nullptr;
+
+  void layout(cv::Mat img,
+              std::vector<StructurePredictResult> &structure_result);
+
+  void table(cv::Mat img, StructurePredictResult &structure_result);

-  void table(cv::Mat img, StructurePredictResult &structure_result,
-             std::vector<double> &time_info_table,
-             std::vector<double> &time_info_det,
-             std::vector<double> &time_info_rec,
-             std::vector<double> &time_info_cls);
  std::string rebuild_table(std::vector<std::string> rec_html_tags,
                            std::vector<std::vector<int>> rec_boxes,
                            std::vector<OCRPredictResult> &ocr_result);

-  float iou(std::vector<int> &box1, std::vector<int> &box2);
  float dis(std::vector<int> &box1, std::vector<int> &box2);

  static bool comparison_dis(const std::vector<float> &dis1,
@@ -14,24 +14,9 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include "include/clipper.h"
 #include "include/utility.h"

-using namespace std;
-
 namespace PaddleOCR {

 class DBPostProcessor {
@@ -106,4 +91,27 @@ private:
  std::string beg = "sos";
 };

+class PicodetPostProcessor {
+public:
+  void init(std::string label_path, const double score_threshold = 0.4,
+            const double nms_threshold = 0.5,
+            const std::vector<int> &fpn_stride = {8, 16, 32, 64});
+  void Run(std::vector<StructurePredictResult> &results,
+           std::vector<std::vector<float>> outs, std::vector<int> ori_shape,
+           std::vector<int> resize_shape, int eg_max);
+  std::vector<int> fpn_stride_ = {8, 16, 32, 64};
+
+private:
+  StructurePredictResult disPred2Bbox(std::vector<float> bbox_pred, int label,
+                                      float score, int x, int y, int stride,
+                                      std::vector<int> im_shape, int reg_max);
+  void nms(std::vector<StructurePredictResult> &input_boxes,
+           float nms_threshold);
+
+  std::vector<std::string> label_list_;
+  double score_threshold_ = 0.4;
+  double nms_threshold_ = 0.5;
+  int num_class_ = 5;
+};
+
 } // namespace PaddleOCR
@@ -14,21 +14,12 @@

 #pragma once

+#include <iostream>
+#include <vector>
+
 #include "opencv2/core.hpp"
 #include "opencv2/imgcodecs.hpp"
 #include "opencv2/imgproc.hpp"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
-using namespace std;
-using namespace paddle;

 namespace PaddleOCR {

@@ -51,9 +42,9 @@ public:

 class ResizeImgType0 {
 public:
-  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, string limit_type,
-                   int limit_side_len, float &ratio_h, float &ratio_w,
-                   bool use_tensorrt);
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img,
+                   std::string limit_type, int limit_side_len, float &ratio_h,
+                   float &ratio_w, bool use_tensorrt);
 };

 class CrnnResizeImg {
@@ -82,4 +73,10 @@ public:
                   const int max_len = 488);
 };

+class Resize {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, const int h,
+                   const int w);
+};
+
 } // namespace PaddleOCR
@@ -0,0 +1,78 @@
+// Copyright (c) 2020 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 "paddle_api.h"
+#include "paddle_inference_api.h"
+
+#include <include/postprocess_op.h>
+#include <include/preprocess_op.h>
+
+namespace PaddleOCR {
+
+class StructureLayoutRecognizer {
+public:
+  explicit StructureLayoutRecognizer(
+      const std::string &model_dir, const bool &use_gpu, const int &gpu_id,
+      const int &gpu_mem, const int &cpu_math_library_num_threads,
+      const bool &use_mkldnn, const std::string &label_path,
+      const bool &use_tensorrt, const std::string &precision,
+      const double &layout_score_threshold,
+      const double &layout_nms_threshold) {
+    this->use_gpu_ = use_gpu;
+    this->gpu_id_ = gpu_id;
+    this->gpu_mem_ = gpu_mem;
+    this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
+    this->use_mkldnn_ = use_mkldnn;
+    this->use_tensorrt_ = use_tensorrt;
+    this->precision_ = precision;
+
+    this->post_processor_.init(label_path, layout_score_threshold,
+                               layout_nms_threshold);
+    LoadModel(model_dir);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir);
+
+  void Run(cv::Mat img, std::vector<StructurePredictResult> &result,
+           std::vector<double> &times);
+
+private:
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
+
+  bool use_gpu_ = false;
+  int gpu_id_ = 0;
+  int gpu_mem_ = 4000;
+  int cpu_math_library_num_threads_ = 4;
+  bool use_mkldnn_ = false;
+
+  std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+  bool is_scale_ = true;
+
+  bool use_tensorrt_ = false;
+  std::string precision_ = "fp32";
+
+  // pre-process
+  Resize resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+
+  // post-process
+  PicodetPostProcessor post_processor_;
+};
+
+} // namespace PaddleOCR
@@ -14,26 +14,11 @@

 #pragma once

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>

 #include <include/postprocess_op.h>
 #include <include/preprocess_op.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;

 namespace PaddleOCR {

@@ -42,7 +27,7 @@ public:
  explicit StructureTableRecognizer(
      const std::string &model_dir, const bool &use_gpu, const int &gpu_id,
      const int &gpu_mem, const int &cpu_math_library_num_threads,
-      const bool &use_mkldnn, const string &label_path,
+      const bool &use_mkldnn, const std::string &label_path,
      const bool &use_tensorrt, const std::string &precision,
      const int &table_batch_num, const int &table_max_len,
      const bool &merge_no_span_structure) {
@@ -70,7 +55,7 @@ public:
           std::vector<double> &times);

 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;

  bool use_gpu_ = false;
  int gpu_id_ = 0;
@@ -41,12 +41,13 @@ struct OCRPredictResult {
 };

 struct StructurePredictResult {
-  std::vector<int> box;
+  std::vector<float> box;
  std::vector<std::vector<int>> cell_box;
  std::string type;
  std::vector<OCRPredictResult> text_res;
  std::string html;
  float html_score = -1;
+  float confidence;
 };

 class Utility {
@@ -82,13 +83,20 @@ public:

  static void print_result(const std::vector<OCRPredictResult> &ocr_result);

-  static cv::Mat crop_image(cv::Mat &img, std::vector<int> &area);
+  static cv::Mat crop_image(cv::Mat &img, const std::vector<int> &area);
+  static cv::Mat crop_image(cv::Mat &img, const std::vector<float> &area);

  static void sorted_boxes(std::vector<OCRPredictResult> &ocr_result);

  static std::vector<int> xyxyxyxy2xyxy(std::vector<std::vector<int>> &box);
  static std::vector<int> xyxyxyxy2xyxy(std::vector<int> &box);

+  static float fast_exp(float x);
+  static std::vector<float>
+  activation_function_softmax(std::vector<float> &src);
+  static float iou(std::vector<int> &box1, std::vector<int> &box2);
+  static float iou(std::vector<float> &box1, std::vector<float> &box2);
+
 private:
  static bool comparison_box(const OCRPredictResult &result1,
                             const OCRPredictResult &result2) {
@@ -174,6 +174,9 @@ inference/
 |-- table
 |   |--inference.pdiparams
 |   |--inference.pdmodel
+|-- layout
+|   |--inference.pdiparams
+|   |--inference.pdmodel
 ```


@@ -278,8 +281,30 @@ Specifically,
    --cls=true \
 ```

+##### 7. layout+table
+```shell
+./build/ppocr --det_model_dir=inference/det_db \
+    --rec_model_dir=inference/rec_rcnn \
+    --table_model_dir=inference/table \
+    --image_dir=../../ppstructure/docs/table/table.jpg \
+    --layout_model_dir=inference/layout \
+    --type=structure \
+    --table=true \
+    --layout=true
+```

-##### 7. table
+##### 8. layout
+```shell
+./build/ppocr --layout_model_dir=inference/layout \
+    --image_dir=../../ppstructure/docs/table/1.png \
+    --type=structure \
+    --table=false \
+    --layout=true \
+    --det=false \
+    --rec=false
+```
+
+##### 9. table
 ```shell
 ./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
@@ -343,6 +368,16 @@ More parameters are as follows,
 |rec_img_h|int|48|image height of recognition|
 |rec_img_w|int|320|image width of recognition|

+- Layout related parameters
+
+|parameter|data type|default|meaning|
+| :---: | :---: | :---: | :---: |
+|layout_model_dir|string|-| Address of layout inference model|
+|layout_dict_path|string|../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt|dictionary file|
+|layout_score_threshold|float|0.5|Threshold of score.|
+|layout_nms_threshold|float|0.5|Threshold of nms.|
+
+
 - Table recognition related parameters

 |parameter|data type|default|meaning|
@@ -368,11 +403,51 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```

- table
+- layout+table

 ```bash
-predict img: ../../ppstructure/docs/table/table.jpg
-0       type: table, region: [0,0,371,293], res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN [3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>-</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td>-</td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+predict img: ../../ppstructure/docs/table/1.png
+0       type: text, region: [12,729,410,848], score: 0.781044, res: count of ocr result is : 7
+********** print ocr result **********
+0       det boxes: [[4,1],[79,1],[79,12],[4,12]] rec text: CTW1500. rec score: 0.769472
+...
+6       det boxes: [[4,99],[391,99],[391,112],[4,112]] rec text: sate-of-the-artmethods[12.34.36l.ourapproachachieves rec score: 0.90414
+********** end print ocr result **********
+1       type: text, region: [69,342,342,359], score: 0.703666, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[269,2],[269,13],[8,13]] rec text: Table6.Experimentalresults on CTW-1500 rec score: 0.890454
+********** end print ocr result **********
+2       type: text, region: [70,316,706,332], score: 0.659738, res: count of ocr result is : 2
+********** print ocr result **********
+0       det boxes: [[373,2],[630,2],[630,11],[373,11]] rec text: oroposals.andthegreencontoursarefinal rec score: 0.919729
+1       det boxes: [[8,3],[357,3],[357,11],[8,11]] rec text: Visualexperimentalresultshebluecontoursareboundar rec score: 0.915963
+********** end print ocr result **********
+3       type: text, region: [489,342,789,359], score: 0.630538, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[294,2],[294,14],[8,14]] rec text: Table7.Experimentalresults onMSRA-TD500 rec score: 0.942251
+********** end print ocr result **********
+4       type: text, region: [444,751,841,848], score: 0.607345, res: count of ocr result is : 5
+********** print ocr result **********
+0       det boxes: [[19,3],[389,3],[389,17],[19,17]] rec text: Inthispaper,weproposeanovel adaptivebound rec score: 0.941031
+1       det boxes: [[4,22],[390,22],[390,36],[4,36]] rec text: aryproposalnetworkforarbitraryshapetextdetection rec score: 0.960172
+2       det boxes: [[4,42],[392,42],[392,56],[4,56]] rec text: whichadoptanboundaryproposalmodeltogeneratecoarse rec score: 0.934647
+3       det boxes: [[4,61],[389,61],[389,75],[4,75]] rec text: ooundaryproposals,andthenadoptanadaptiveboundary rec score: 0.946296
+4       det boxes: [[5,80],[387,80],[387,93],[5,93]] rec text: leformationmodelcombinedwithGCNandRNNtoper rec score: 0.952401
+********** end print ocr result **********
+5       type: title, region: [444,705,564,724], score: 0.785429, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[6,2],[113,2],[113,14],[6,14]] rec text: 5.Conclusion rec score: 0.856903
+********** end print ocr result **********
+6       type: table, region: [14,360,402,711], score: 0.963643, res: <html><body><table><thead><tr><td>Methods</td><td>Ext</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>TextSnake [18]</td><td>Syn</td><td>85.3</td><td>67.9</td><td>75.6</td><td></td></tr><tr><td>CSE [17]</td><td>MiLT</td><td>76.1</td><td>78.7</td><td>77.4</td><td>0.38</td></tr><tr><td>LOMO[40]</td><td>Syn</td><td>76.5</td><td>85.7</td><td>80.8</td><td>4.4</td></tr><tr><td>ATRR[35]</td><td>Sy-</td><td>80.2</td><td>80.1</td><td>80.1</td><td>-</td></tr><tr><td>SegLink++ [28]</td><td>Syn</td><td>79.8</td><td>82.8</td><td>81.3</td><td>-</td></tr><tr><td>TextField [37]</td><td>Syn</td><td>79.8</td><td>83.0</td><td>81.4</td><td>6.0</td></tr><tr><td>MSR[38]</td><td>Syn</td><td>79.0</td><td>84.1</td><td>81.5</td><td>4.3</td></tr><tr><td>PSENet-1s [33]</td><td>MLT</td><td>79.7</td><td>84.8</td><td>82.2</td><td>3.9</td></tr><tr><td>DB [12]</td><td>Syn</td><td>80.2</td><td>86.9</td><td>83.4</td><td>22.0</td></tr><tr><td>CRAFT [2]</td><td>Syn</td><td>81.1</td><td>86.0</td><td>83.5</td><td>-</td></tr><tr><td>TextDragon [5]</td><td>MLT+</td><td>82.8</td><td>84.5</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>Syn</td><td>81.2</td><td>86.4</td><td>83.7</td><td>39.8</td></tr><tr><td>ContourNet [36]</td><td></td><td>84.1</td><td>83.7</td><td>83.9</td><td>4.5</td></tr><tr><td>DRRG [41]</td><td>MLT</td><td>83.02</td><td>85.93</td><td>84.45</td><td>-</td></tr><tr><td>TextPerception[23]</td><td>Syn</td><td>81.9</td><td>87.5</td><td>84.6</td><td></td></tr><tr><td>Ours</td><td> Syn</td><td>80.57</td><td>87.66</td><td>83.97</td><td>12.08</td></tr><tr><td>Ours</td><td></td><td>81.45</td><td>87.81</td><td>84.51</td><td>12.15</td></tr><tr><td>Ours</td><td>MLT</td><td>83.60</td><td>86.45</td><td>85.00</td><td>12.21</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//6_1.png
+7       type: table, region: [462,359,820,657], score: 0.953917, res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN[3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>:</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//7_1.png
+8       type: figure, region: [14,3,836,310], score: 0.969443, res: count of ocr result is : 26
+********** print ocr result **********
+0       det boxes: [[506,14],[539,15],[539,22],[506,21]] rec text: E rec score: 0.318073
+...
+25      det boxes: [[680,290],[759,288],[759,303],[680,305]] rec text: (d) CTW1500 rec score: 0.95911
+********** end print ocr result **********
 ```

 <a name="3"></a>
@@ -184,6 +184,9 @@ inference/
 |-- table
 |   |--inference.pdiparams
 |   |--inference.pdmodel
+|-- layout
+|   |--inference.pdiparams
+|   |--inference.pdmodel
 ```

 <a name="22"></a>
@@ -288,7 +291,30 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
    --cls=true \
 ```

-##### 7. 表格识别
+##### 7. 版面分析+表格识别
+```shell
+./build/ppocr --det_model_dir=inference/det_db \
+    --rec_model_dir=inference/rec_rcnn \
+    --table_model_dir=inference/table \
+    --image_dir=../../ppstructure/docs/table/table.jpg \
+    --layout_model_dir=inference/layout \
+    --type=structure \
+    --table=true \
+    --layout=true
+```
+
+##### 8. 版面分析
+```shell
+./build/ppocr --layout_model_dir=inference/layout \
+    --image_dir=../../ppstructure/docs/table/1.png \
+    --type=structure \
+    --table=false \
+    --layout=true \
+    --det=false \
+    --rec=false
+```
+
+##### 9. 表格识别
 ```shell
 ./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
@@ -352,12 +378,22 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |rec_img_w|int|320|文字识别模型输入图像宽度|


+- 版面分析模型相关
+
+|参数名称|类型|默认参数|意义|
+| :---: | :---: | :---: | :---: |
+|layout_model_dir|string|-|版面分析模型inference model地址|
+|layout_dict_path|string|../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt|字典文件|
+|layout_score_threshold|float|0.5|检测框的分数阈值|
+|layout_nms_threshold|float|0.5|nms的阈值|
+
+
 - 表格识别模型相关

 |参数名称|类型|默认参数|意义|
 | :---: | :---: | :---: | :---: |
 |table_model_dir|string|-|表格识别模型inference model地址|
-|table_char_dict_path|string|../../ppocr/utils/dict/table_structure_dict.txt|字典文件|
+|table_char_dict_path|string|../../ppocr/utils/dict/table_structure_dict_ch.txt|字典文件|
 |table_max_len|int|488|表格识别模型输入图像长边大小，最终网络输入图像大小为（table_max_len，table_max_len）|
 |merge_no_span_structure|bool|true|是否合并<td> 和 </td> 为<td></td>|

@@ -378,11 +414,51 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```

- table
+- layout+table

 ```bash
-predict img: ../../ppstructure/docs/table/table.jpg
-0       type: table, region: [0,0,371,293], res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN [3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>-</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td>-</td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+predict img: ../../ppstructure/docs/table/1.png
+0       type: text, region: [12,729,410,848], score: 0.781044, res: count of ocr result is : 7
+********** print ocr result **********
+0       det boxes: [[4,1],[79,1],[79,12],[4,12]] rec text: CTW1500. rec score: 0.769472
+...
+6       det boxes: [[4,99],[391,99],[391,112],[4,112]] rec text: sate-of-the-artmethods[12.34.36l.ourapproachachieves rec score: 0.90414
+********** end print ocr result **********
+1       type: text, region: [69,342,342,359], score: 0.703666, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[269,2],[269,13],[8,13]] rec text: Table6.Experimentalresults on CTW-1500 rec score: 0.890454
+********** end print ocr result **********
+2       type: text, region: [70,316,706,332], score: 0.659738, res: count of ocr result is : 2
+********** print ocr result **********
+0       det boxes: [[373,2],[630,2],[630,11],[373,11]] rec text: oroposals.andthegreencontoursarefinal rec score: 0.919729
+1       det boxes: [[8,3],[357,3],[357,11],[8,11]] rec text: Visualexperimentalresultshebluecontoursareboundar rec score: 0.915963
+********** end print ocr result **********
+3       type: text, region: [489,342,789,359], score: 0.630538, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[294,2],[294,14],[8,14]] rec text: Table7.Experimentalresults onMSRA-TD500 rec score: 0.942251
+********** end print ocr result **********
+4       type: text, region: [444,751,841,848], score: 0.607345, res: count of ocr result is : 5
+********** print ocr result **********
+0       det boxes: [[19,3],[389,3],[389,17],[19,17]] rec text: Inthispaper,weproposeanovel adaptivebound rec score: 0.941031
+1       det boxes: [[4,22],[390,22],[390,36],[4,36]] rec text: aryproposalnetworkforarbitraryshapetextdetection rec score: 0.960172
+2       det boxes: [[4,42],[392,42],[392,56],[4,56]] rec text: whichadoptanboundaryproposalmodeltogeneratecoarse rec score: 0.934647
+3       det boxes: [[4,61],[389,61],[389,75],[4,75]] rec text: ooundaryproposals,andthenadoptanadaptiveboundary rec score: 0.946296
+4       det boxes: [[5,80],[387,80],[387,93],[5,93]] rec text: leformationmodelcombinedwithGCNandRNNtoper rec score: 0.952401
+********** end print ocr result **********
+5       type: title, region: [444,705,564,724], score: 0.785429, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[6,2],[113,2],[113,14],[6,14]] rec text: 5.Conclusion rec score: 0.856903
+********** end print ocr result **********
+6       type: table, region: [14,360,402,711], score: 0.963643, res: <html><body><table><thead><tr><td>Methods</td><td>Ext</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>TextSnake [18]</td><td>Syn</td><td>85.3</td><td>67.9</td><td>75.6</td><td></td></tr><tr><td>CSE [17]</td><td>MiLT</td><td>76.1</td><td>78.7</td><td>77.4</td><td>0.38</td></tr><tr><td>LOMO[40]</td><td>Syn</td><td>76.5</td><td>85.7</td><td>80.8</td><td>4.4</td></tr><tr><td>ATRR[35]</td><td>Sy-</td><td>80.2</td><td>80.1</td><td>80.1</td><td>-</td></tr><tr><td>SegLink++ [28]</td><td>Syn</td><td>79.8</td><td>82.8</td><td>81.3</td><td>-</td></tr><tr><td>TextField [37]</td><td>Syn</td><td>79.8</td><td>83.0</td><td>81.4</td><td>6.0</td></tr><tr><td>MSR[38]</td><td>Syn</td><td>79.0</td><td>84.1</td><td>81.5</td><td>4.3</td></tr><tr><td>PSENet-1s [33]</td><td>MLT</td><td>79.7</td><td>84.8</td><td>82.2</td><td>3.9</td></tr><tr><td>DB [12]</td><td>Syn</td><td>80.2</td><td>86.9</td><td>83.4</td><td>22.0</td></tr><tr><td>CRAFT [2]</td><td>Syn</td><td>81.1</td><td>86.0</td><td>83.5</td><td>-</td></tr><tr><td>TextDragon [5]</td><td>MLT+</td><td>82.8</td><td>84.5</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>Syn</td><td>81.2</td><td>86.4</td><td>83.7</td><td>39.8</td></tr><tr><td>ContourNet [36]</td><td></td><td>84.1</td><td>83.7</td><td>83.9</td><td>4.5</td></tr><tr><td>DRRG [41]</td><td>MLT</td><td>83.02</td><td>85.93</td><td>84.45</td><td>-</td></tr><tr><td>TextPerception[23]</td><td>Syn</td><td>81.9</td><td>87.5</td><td>84.6</td><td></td></tr><tr><td>Ours</td><td> Syn</td><td>80.57</td><td>87.66</td><td>83.97</td><td>12.08</td></tr><tr><td>Ours</td><td></td><td>81.45</td><td>87.81</td><td>84.51</td><td>12.15</td></tr><tr><td>Ours</td><td>MLT</td><td>83.60</td><td>86.45</td><td>85.00</td><td>12.21</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//6_1.png
+7       type: table, region: [462,359,820,657], score: 0.953917, res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN[3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>:</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//7_1.png
+8       type: figure, region: [14,3,836,310], score: 0.969443, res: count of ocr result is : 26
+********** print ocr result **********
+0       det boxes: [[506,14],[539,15],[539,22],[506,21]] rec text: E rec score: 0.318073
+...
+25      det boxes: [[680,290],[759,288],[759,303],[680,305]] rec text: (d) CTW1500 rec score: 0.95911
+********** end print ocr result **********
 ```

 <a name="3"></a>
@@ -51,6 +51,13 @@ DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
 DEFINE_int32(rec_img_h, 48, "rec image height");
 DEFINE_int32(rec_img_w, 320, "rec image width");

+// layout model related
+DEFINE_string(layout_model_dir, "", "Path of table layout inference model.");
+DEFINE_string(layout_dict_path,
+              "../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt",
+              "Path of dictionary.");
+DEFINE_double(layout_score_threshold, 0.5, "Threshold of score.");
+DEFINE_double(layout_nms_threshold, 0.5, "Threshold of nms.");
 // structure model related
 DEFINE_string(table_model_dir, "", "Path of table struture inference model.");
 DEFINE_int32(table_max_len, 488, "max len size of input image.");
@@ -65,4 +72,5 @@ DEFINE_string(table_char_dict_path,
 DEFINE_bool(det, true, "Whether use det in forward.");
 DEFINE_bool(rec, true, "Whether use rec in forward.");
 DEFINE_bool(cls, false, "Whether use cls in forward.");
-DEFINE_bool(table, false, "Whether use table structure in forward.");
+DEFINE_bool(table, false, "Whether use table structure in forward.");
+DEFINE_bool(layout, false, "Whether use layout analysis in forward.");
@@ -65,9 +65,18 @@ void check_params() {
      exit(1);
    }
  }
+  if (FLAGS_layout) {
+    if (FLAGS_layout_model_dir.empty() || FLAGS_image_dir.empty()) {
+      std::cout << "Usage[layout]: ./ppocr "
+                << "--layout_model_dir=/PATH/TO/LAYOUT_INFERENCE_MODEL/ "
+                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
+      exit(1);
+    }
+  }
  if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
      FLAGS_precision != "int8") {
-    cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
+    std::cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. "
+              << std::endl;
    exit(1);
  }
 }
@@ -75,71 +84,94 @@ void check_params() {
 void ocr(std::vector<cv::String> &cv_all_img_names) {
  PPOCR ocr = PPOCR();

-  std::vector<std::vector<OCRPredictResult>> ocr_results =
-      ocr.ocr(cv_all_img_names, FLAGS_det, FLAGS_rec, FLAGS_cls);
+  if (FLAGS_benchmark) {
+    ocr.reset_timer();
+  }

+  std::vector<cv::Mat> img_list;
+  std::vector<cv::String> img_names;
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    if (FLAGS_benchmark) {
-      cout << cv_all_img_names[i] << '\t';
-      if (FLAGS_rec && FLAGS_det) {
-        Utility::print_result(ocr_results[i]);
-      } else if (FLAGS_det) {
-        for (int n = 0; n < ocr_results[i].size(); n++) {
-          for (int m = 0; m < ocr_results[i][n].box.size(); m++) {
-            cout << ocr_results[i][n].box[m][0] << ' '
-                 << ocr_results[i][n].box[m][1] << ' ';
-          }
-        }
-        cout << endl;
-      } else {
-        Utility::print_result(ocr_results[i]);
-      }
-    } else {
-      cout << cv_all_img_names[i] << "\n";
-      Utility::print_result(ocr_results[i]);
-      if (FLAGS_visualize && FLAGS_det) {
-        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;
-          exit(1);
-        }
-        std::string file_name = Utility::basename(cv_all_img_names[i]);
-
-        Utility::VisualizeBboxes(srcimg, ocr_results[i],
-                                 FLAGS_output + "/" + file_name);
-      }
-      cout << "***************************" << endl;
+    cv::Mat img = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+    if (!img.data) {
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << std::endl;
+      continue;
    }
+    img_list.push_back(img);
+    img_names.push_back(cv_all_img_names[i]);
+  }
+
+  std::vector<std::vector<OCRPredictResult>> ocr_results =
+      ocr.ocr(img_list, FLAGS_det, FLAGS_rec, FLAGS_cls);
+
+  for (int i = 0; i < img_names.size(); ++i) {
+    std::cout << "predict img: " << cv_all_img_names[i] << std::endl;
+    Utility::print_result(ocr_results[i]);
+    if (FLAGS_visualize && FLAGS_det) {
+      std::string file_name = Utility::basename(img_names[i]);
+      cv::Mat srcimg = img_list[i];
+      Utility::VisualizeBboxes(srcimg, ocr_results[i],
+                               FLAGS_output + "/" + file_name);
+    }
+  }
+  if (FLAGS_benchmark) {
+    ocr.benchmark_log(cv_all_img_names.size());
  }
 }

 void structure(std::vector<cv::String> &cv_all_img_names) {
  PaddleOCR::PaddleStructure engine = PaddleOCR::PaddleStructure();
-  std::vector<std::vector<StructurePredictResult>> structure_results =
-      engine.structure(cv_all_img_names, false, FLAGS_table);
-  for (int i = 0; i < cv_all_img_names.size(); i++) {
-    cout << "predict img: " << cv_all_img_names[i] << endl;
-    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-    for (int j = 0; j < structure_results[i].size(); j++) {
-      std::cout << j << "\ttype: " << structure_results[i][j].type
-                << ", region: [";
-      std::cout << structure_results[i][j].box[0] << ","
-                << structure_results[i][j].box[1] << ","
-                << structure_results[i][j].box[2] << ","
-                << structure_results[i][j].box[3] << "], res: ";
-      if (structure_results[i][j].type == "table") {
-        std::cout << structure_results[i][j].html << std::endl;
-        std::string file_name = Utility::basename(cv_all_img_names[i]);

-        Utility::VisualizeBboxes(srcimg, structure_results[i][j],
-                                 FLAGS_output + "/" + std::to_string(j) + "_" +
-                                     file_name);
+  if (FLAGS_benchmark) {
+    engine.reset_timer();
+  }
+
+  for (int i = 0; i < cv_all_img_names.size(); i++) {
+    std::cout << "predict img: " << cv_all_img_names[i] << std::endl;
+    cv::Mat img = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+    if (!img.data) {
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << std::endl;
+      continue;
+    }
+
+    std::vector<StructurePredictResult> structure_results = engine.structure(
+        img, FLAGS_layout, FLAGS_table, FLAGS_det && FLAGS_rec);
+
+    for (int j = 0; j < structure_results.size(); j++) {
+      std::cout << j << "\ttype: " << structure_results[j].type
+                << ", region: [";
+      std::cout << structure_results[j].box[0] << ","
+                << structure_results[j].box[1] << ","
+                << structure_results[j].box[2] << ","
+                << structure_results[j].box[3] << "], score: ";
+      std::cout << structure_results[j].confidence << ", res: ";
+
+      if (structure_results[j].type == "table") {
+        std::cout << structure_results[j].html << std::endl;
+        if (structure_results[j].cell_box.size() > 0 && FLAGS_visualize) {
+          std::string file_name = Utility::basename(cv_all_img_names[i]);
+
+          Utility::VisualizeBboxes(img, structure_results[j],
+                                   FLAGS_output + "/" + std::to_string(j) +
+                                       "_" + file_name);
+        }
      } else {
-        Utility::print_result(structure_results[i][j].text_res);
+        std::cout << "count of ocr result is : "
+                  << structure_results[j].text_res.size() << std::endl;
+        if (structure_results[j].text_res.size() > 0) {
+          std::cout << "********** print ocr result "
+                    << "**********" << std::endl;
+          Utility::print_result(structure_results[j].text_res);
+          std::cout << "********** end print ocr result "
+                    << "**********" << std::endl;
+        }
      }
    }
  }
+  if (FLAGS_benchmark) {
+    engine.benchmark_log(cv_all_img_names.size());
+  }
 }

 int main(int argc, char **argv) {
@@ -149,19 +181,22 @@ int main(int argc, char **argv) {

  if (!Utility::PathExists(FLAGS_image_dir)) {
    std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
-              << endl;
+              << std::endl;
    exit(1);
  }

  std::vector<cv::String> cv_all_img_names;
  cv::glob(FLAGS_image_dir, cv_all_img_names);
-  std::cout << "total images num: " << cv_all_img_names.size() << endl;
+  std::cout << "total images num: " << cv_all_img_names.size() << std::endl;

+  if (!Utility::PathExists(FLAGS_output)) {
+    Utility::CreateDir(FLAGS_output);
+  }
  if (FLAGS_type == "ocr") {
    ocr(cv_all_img_names);
  } else if (FLAGS_type == "structure") {
    structure(cv_all_img_names);
  } else {
-    std::cout << "only value in ['ocr','structure'] is supported" << endl;
+    std::cout << "only value in ['ocr','structure'] is supported" << std::endl;
  }
 }
@@ -32,7 +32,7 @@ void Classifier::Run(std::vector<cv::Mat> img_list,
  for (int beg_img_no = 0; beg_img_no < img_num;
       beg_img_no += this->cls_batch_num_) {
    auto preprocess_start = std::chrono::steady_clock::now();
-    int end_img_no = min(img_num, beg_img_no + this->cls_batch_num_);
+    int end_img_no = std::min(img_num, beg_img_no + this->cls_batch_num_);
    int batch_num = end_img_no - beg_img_no;
    // preprocess
    std::vector<cv::Mat> norm_img_batch;
@@ -97,7 +97,7 @@ void Classifier::Run(std::vector<cv::Mat> img_list,
 }

 void Classifier::LoadModel(const std::string &model_dir) {
-  AnalysisConfig config;
+  paddle_infer::Config config;
  config.SetModel(model_dir + "/inference.pdmodel",
                  model_dir + "/inference.pdiparams");

@@ -112,9 +112,9 @@ void Classifier::LoadModel(const std::string &model_dir) {
        precision = paddle_infer::Config::Precision::kInt8;
      }
      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
-      if (!Utility::PathExists("./trt_cls_shape.txt")){
+      if (!Utility::PathExists("./trt_cls_shape.txt")) {
        config.CollectShapeRangeInfo("./trt_cls_shape.txt");
-      } else { 
+      } else {
        config.EnableTunedTensorRtDynamicShape("./trt_cls_shape.txt", true);
      }
    }
@@ -136,6 +136,6 @@ void Classifier::LoadModel(const std::string &model_dir) {
  config.EnableMemoryOptim();
  config.DisableGlogInfo();

-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 } // namespace PaddleOCR
@@ -33,12 +33,11 @@ void DBDetector::LoadModel(const std::string &model_dir) {
        precision = paddle_infer::Config::Precision::kInt8;
      }
      config.EnableTensorRtEngine(1 << 30, 1, 20, precision, false, false);
-      if (!Utility::PathExists("./trt_det_shape.txt")){
+      if (!Utility::PathExists("./trt_det_shape.txt")) {
        config.CollectShapeRangeInfo("./trt_det_shape.txt");
-      } else { 
+      } else {
        config.EnableTunedTensorRtDynamicShape("./trt_det_shape.txt", true);
      }
-      
    }
  } else {
    config.DisableGpu();
@@ -59,7 +58,7 @@ void DBDetector::LoadModel(const std::string &model_dir) {
  config.EnableMemoryOptim();
  // config.DisableGlogInfo();

-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }

 void DBDetector::Run(cv::Mat &img,
@@ -37,7 +37,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
  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_);
+    int end_img_no = std::min(img_num, beg_img_no + this->rec_batch_num_);
    int batch_num = end_img_no - beg_img_no;
    int imgH = this->rec_image_shape_[1];
    int imgW = this->rec_image_shape_[2];
@@ -46,7 +46,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
      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);
+      max_wh_ratio = std::max(max_wh_ratio, wh_ratio);
    }

    int batch_width = imgW;
@@ -60,7 +60,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
      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);
+      batch_width = std::max(resize_img.cols, batch_width);
    }

    std::vector<float> input(batch_num * 3 * imgH * batch_width, 0.0f);
@@ -115,7 +115,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
        last_index = argmax_idx;
      }
      score /= count;
-      if (isnan(score)) {
+      if (std::isnan(score)) {
        continue;
      }
      rec_texts[indices[beg_img_no + m]] = str_res;
@@ -130,7 +130,6 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
 }

 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
-  //   AnalysisConfig config;
  paddle_infer::Config config;
  config.SetModel(model_dir + "/inference.pdmodel",
                  model_dir + "/inference.pdiparams");
@@ -147,12 +146,11 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
      if (this->precision_ == "int8") {
        precision = paddle_infer::Config::Precision::kInt8;
      }
-      if (!Utility::PathExists("./trt_rec_shape.txt")){
+      if (!Utility::PathExists("./trt_rec_shape.txt")) {
        config.CollectShapeRangeInfo("./trt_rec_shape.txt");
-      } else { 
+      } else {
        config.EnableTunedTensorRtDynamicShape("./trt_rec_shape.txt", true);
      }
-      
    }
  } else {
    config.DisableGpu();
@@ -177,7 +175,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
  config.EnableMemoryOptim();
  //   config.DisableGlogInfo();

-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }

 } // namespace PaddleOCR
@@ -16,7 +16,7 @@
 #include <include/paddleocr.h>

 #include "auto_log/autolog.h"
-#include <numeric>
+
 namespace PaddleOCR {

 PPOCR::PPOCR() {
@@ -44,8 +44,71 @@ PPOCR::PPOCR() {
  }
 };

-void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+std::vector<std::vector<OCRPredictResult>>
+PPOCR::ocr(std::vector<cv::Mat> img_list, bool det, bool rec, bool cls) {
+  std::vector<std::vector<OCRPredictResult>> ocr_results;
+
+  if (!det) {
+    std::vector<OCRPredictResult> ocr_result;
+    ocr_result.resize(img_list.size());
+    if (cls && this->classifier_ != nullptr) {
+      this->cls(img_list, ocr_result);
+      for (int i = 0; i < img_list.size(); i++) {
+        if (ocr_result[i].cls_label % 2 == 1 &&
+            ocr_result[i].cls_score > this->classifier_->cls_thresh) {
+          cv::rotate(img_list[i], img_list[i], 1);
+        }
+      }
+    }
+    if (rec) {
+      this->rec(img_list, ocr_result);
+    }
+    for (int i = 0; i < ocr_result.size(); ++i) {
+      std::vector<OCRPredictResult> ocr_result_tmp;
+      ocr_result_tmp.push_back(ocr_result[i]);
+      ocr_results.push_back(ocr_result_tmp);
+    }
+  } else {
+    for (int i = 0; i < img_list.size(); ++i) {
+      std::vector<OCRPredictResult> ocr_result =
+          this->ocr(img_list[i], true, rec, cls);
+      ocr_results.push_back(ocr_result);
+    }
+  }
+  return ocr_results;
+}
+
+std::vector<OCRPredictResult> PPOCR::ocr(cv::Mat img, bool det, bool rec,
+                                         bool cls) {
+
+  std::vector<OCRPredictResult> ocr_result;
+  // det
+  this->det(img, ocr_result);
+  // crop image
+  std::vector<cv::Mat> img_list;
+  for (int j = 0; j < ocr_result.size(); j++) {
+    cv::Mat crop_img;
+    crop_img = Utility::GetRotateCropImage(img, ocr_result[j].box);
+    img_list.push_back(crop_img);
+  }
+  // cls
+  if (cls && this->classifier_ != nullptr) {
+    this->cls(img_list, ocr_result);
+    for (int i = 0; i < img_list.size(); i++) {
+      if (ocr_result[i].cls_label % 2 == 1 &&
+          ocr_result[i].cls_score > this->classifier_->cls_thresh) {
+        cv::rotate(img_list[i], img_list[i], 1);
+      }
+    }
+  }
+  // rec
+  if (rec) {
+    this->rec(img_list, ocr_result);
+  }
+  return ocr_result;
+}
+
+void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results) {
  std::vector<std::vector<std::vector<int>>> boxes;
  std::vector<double> det_times;

@@ -58,14 +121,13 @@ void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
  }
  // sort boex from top to bottom, from left to right
  Utility::sorted_boxes(ocr_results);
-  times[0] += det_times[0];
-  times[1] += det_times[1];
-  times[2] += det_times[2];
+  this->time_info_det[0] += det_times[0];
+  this->time_info_det[1] += det_times[1];
+  this->time_info_det[2] += det_times[2];
 }

 void PPOCR::rec(std::vector<cv::Mat> img_list,
-                std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+                std::vector<OCRPredictResult> &ocr_results) {
  std::vector<std::string> rec_texts(img_list.size(), "");
  std::vector<float> rec_text_scores(img_list.size(), 0);
  std::vector<double> rec_times;
@@ -75,14 +137,13 @@ void PPOCR::rec(std::vector<cv::Mat> img_list,
    ocr_results[i].text = rec_texts[i];
    ocr_results[i].score = rec_text_scores[i];
  }
-  times[0] += rec_times[0];
-  times[1] += rec_times[1];
-  times[2] += rec_times[2];
+  this->time_info_rec[0] += rec_times[0];
+  this->time_info_rec[1] += rec_times[1];
+  this->time_info_rec[2] += rec_times[2];
 }

 void PPOCR::cls(std::vector<cv::Mat> img_list,
-                std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+                std::vector<OCRPredictResult> &ocr_results) {
  std::vector<int> cls_labels(img_list.size(), 0);
  std::vector<float> cls_scores(img_list.size(), 0);
  std::vector<double> cls_times;
@@ -92,125 +153,43 @@ void PPOCR::cls(std::vector<cv::Mat> img_list,
    ocr_results[i].cls_label = cls_labels[i];
    ocr_results[i].cls_score = cls_scores[i];
  }
-  times[0] += cls_times[0];
-  times[1] += cls_times[1];
-  times[2] += cls_times[2];
+  this->time_info_cls[0] += cls_times[0];
+  this->time_info_cls[1] += cls_times[1];
+  this->time_info_cls[2] += cls_times[2];
 }

-std::vector<std::vector<OCRPredictResult>>
-PPOCR::ocr(std::vector<cv::String> cv_all_img_names, bool det, bool rec,
-           bool cls) {
-  std::vector<double> time_info_det = {0, 0, 0};
-  std::vector<double> time_info_rec = {0, 0, 0};
-  std::vector<double> time_info_cls = {0, 0, 0};
-  std::vector<std::vector<OCRPredictResult>> ocr_results;
+void PPOCR::reset_timer() {
+  this->time_info_det = {0, 0, 0};
+  this->time_info_rec = {0, 0, 0};
+  this->time_info_cls = {0, 0, 0};
+}

-  if (!det) {
-    std::vector<OCRPredictResult> ocr_result;
-    // read image
-    std::vector<cv::Mat> img_list;
-    for (int i = 0; i < cv_all_img_names.size(); ++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;
-        exit(1);
-      }
-      img_list.push_back(srcimg);
-      OCRPredictResult res;
-      ocr_result.push_back(res);
-    }
-    if (cls && this->classifier_ != nullptr) {
-      this->cls(img_list, ocr_result, time_info_cls);
-      for (int i = 0; i < img_list.size(); i++) {
-        if (ocr_result[i].cls_label % 2 == 1 &&
-            ocr_result[i].cls_score > this->classifier_->cls_thresh) {
-          cv::rotate(img_list[i], img_list[i], 1);
-        }
-      }
-    }
-    if (rec) {
-      this->rec(img_list, ocr_result, time_info_rec);
-    }
-    for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      std::vector<OCRPredictResult> ocr_result_tmp;
-      ocr_result_tmp.push_back(ocr_result[i]);
-      ocr_results.push_back(ocr_result_tmp);
-    }
-  } else {
-    if (!Utility::PathExists(FLAGS_output) && FLAGS_det) {
-      Utility::CreateDir(FLAGS_output);
-    }
-
-    for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      std::vector<OCRPredictResult> ocr_result;
-      if (!FLAGS_benchmark) {
-        cout << "predict img: " << cv_all_img_names[i] << endl;
-      }
-
-      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;
-        exit(1);
-      }
-      // det
-      this->det(srcimg, ocr_result, time_info_det);
-      // crop image
-      std::vector<cv::Mat> img_list;
-      for (int j = 0; j < ocr_result.size(); j++) {
-        cv::Mat crop_img;
-        crop_img = Utility::GetRotateCropImage(srcimg, ocr_result[j].box);
-        img_list.push_back(crop_img);
-      }
-
-      // cls
-      if (cls && this->classifier_ != nullptr) {
-        this->cls(img_list, ocr_result, time_info_cls);
-        for (int i = 0; i < img_list.size(); i++) {
-          if (ocr_result[i].cls_label % 2 == 1 &&
-              ocr_result[i].cls_score > this->classifier_->cls_thresh) {
-            cv::rotate(img_list[i], img_list[i], 1);
-          }
-        }
-      }
-      // rec
-      if (rec) {
-        this->rec(img_list, ocr_result, time_info_rec);
-      }
-      ocr_results.push_back(ocr_result);
-    }
-  }
-  if (FLAGS_benchmark) {
-    this->log(time_info_det, time_info_rec, time_info_cls,
-              cv_all_img_names.size());
-  }
-  return ocr_results;
-} // namespace PaddleOCR
-
-void PPOCR::log(std::vector<double> &det_times, std::vector<double> &rec_times,
-                std::vector<double> &cls_times, int img_num) {
-  if (det_times[0] + det_times[1] + det_times[2] > 0) {
+void PPOCR::benchmark_log(int img_num) {
+  if (this->time_info_det[0] + this->time_info_det[1] + this->time_info_det[2] >
+      0) {
    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
-                           FLAGS_precision, det_times, img_num);
+                           FLAGS_precision, this->time_info_det, img_num);
    autolog_det.report();
  }
-  if (rec_times[0] + rec_times[1] + rec_times[2] > 0) {
+  if (this->time_info_rec[0] + this->time_info_rec[1] + this->time_info_rec[2] >
+      0) {
    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
-                           rec_times, img_num);
+                           this->time_info_rec, img_num);
    autolog_rec.report();
  }
-  if (cls_times[0] + cls_times[1] + cls_times[2] > 0) {
+  if (this->time_info_cls[0] + this->time_info_cls[1] + this->time_info_cls[2] >
+      0) {
    AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                           FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
-                           cls_times, img_num);
+                           this->time_info_cls, img_num);
    autolog_cls.report();
  }
 }
+
 PPOCR::~PPOCR() {
  if (this->detector_ != nullptr) {
    delete this->detector_;
@@ -16,14 +16,19 @@
 #include <include/paddlestructure.h>

 #include "auto_log/autolog.h"
-#include <numeric>
-#include <sys/stat.h>

 namespace PaddleOCR {

 PaddleStructure::PaddleStructure() {
+  if (FLAGS_layout) {
+    this->layout_model_ = new StructureLayoutRecognizer(
+        FLAGS_layout_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
+        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_layout_dict_path,
+        FLAGS_use_tensorrt, FLAGS_precision, FLAGS_layout_score_threshold,
+        FLAGS_layout_nms_threshold);
+  }
  if (FLAGS_table) {
-    this->recognizer_ = new StructureTableRecognizer(
+    this->table_model_ = new StructureTableRecognizer(
        FLAGS_table_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_table_char_dict_path,
        FLAGS_use_tensorrt, FLAGS_precision, FLAGS_table_batch_num,
@@ -31,68 +36,63 @@ PaddleStructure::PaddleStructure() {
  }
 };

-std::vector<std::vector<StructurePredictResult>>
-PaddleStructure::structure(std::vector<cv::String> cv_all_img_names,
-                           bool layout, bool table) {
-  std::vector<double> time_info_det = {0, 0, 0};
-  std::vector<double> time_info_rec = {0, 0, 0};
-  std::vector<double> time_info_cls = {0, 0, 0};
-  std::vector<double> time_info_table = {0, 0, 0};
+std::vector<StructurePredictResult>
+PaddleStructure::structure(cv::Mat srcimg, bool layout, bool table, bool ocr) {
+  cv::Mat img;
+  srcimg.copyTo(img);

-  std::vector<std::vector<StructurePredictResult>> structure_results;
+  std::vector<StructurePredictResult> structure_results;

-  if (!Utility::PathExists(FLAGS_output) && FLAGS_det) {
-    Utility::CreateDir(FLAGS_output);
+  if (layout) {
+    this->layout(img, structure_results);
+  } else {
+    StructurePredictResult res;
+    res.type = "table";
+    res.box = std::vector<float>(4, 0.0);
+    res.box[2] = img.cols;
+    res.box[3] = img.rows;
+    structure_results.push_back(res);
  }
-  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    std::vector<StructurePredictResult> structure_result;
-    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;
-      exit(1);
+  cv::Mat roi_img;
+  for (int i = 0; i < structure_results.size(); i++) {
+    // crop image
+    roi_img = Utility::crop_image(img, structure_results[i].box);
+    if (structure_results[i].type == "table" && table) {
+      this->table(roi_img, structure_results[i]);
+    } else if (ocr) {
+      structure_results[i].text_res = this->ocr(roi_img, true, true, false);
    }
-    if (layout) {
-    } else {
-      StructurePredictResult res;
-      res.type = "table";
-      res.box = std::vector<int>(4, 0);
-      res.box[2] = srcimg.cols;
-      res.box[3] = srcimg.rows;
-      structure_result.push_back(res);
-    }
-    cv::Mat roi_img;
-    for (int i = 0; i < structure_result.size(); i++) {
-      // crop image
-      roi_img = Utility::crop_image(srcimg, structure_result[i].box);
-      if (structure_result[i].type == "table") {
-        this->table(roi_img, structure_result[i], time_info_table,
-                    time_info_det, time_info_rec, time_info_cls);
-      }
-    }
-    structure_results.push_back(structure_result);
  }
+
  return structure_results;
 };

+void PaddleStructure::layout(
+    cv::Mat img, std::vector<StructurePredictResult> &structure_result) {
+  std::vector<double> layout_times;
+  this->layout_model_->Run(img, structure_result, layout_times);
+
+  this->time_info_layout[0] += layout_times[0];
+  this->time_info_layout[1] += layout_times[1];
+  this->time_info_layout[2] += layout_times[2];
+}
+
 void PaddleStructure::table(cv::Mat img,
-                            StructurePredictResult &structure_result,
-                            std::vector<double> &time_info_table,
-                            std::vector<double> &time_info_det,
-                            std::vector<double> &time_info_rec,
-                            std::vector<double> &time_info_cls) {
+                            StructurePredictResult &structure_result) {
  // predict structure
  std::vector<std::vector<std::string>> structure_html_tags;
  std::vector<float> structure_scores(1, 0);
  std::vector<std::vector<std::vector<int>>> structure_boxes;
-  std::vector<double> structure_imes;
+  std::vector<double> structure_times;
  std::vector<cv::Mat> img_list;
  img_list.push_back(img);
-  this->recognizer_->Run(img_list, structure_html_tags, structure_scores,
-                         structure_boxes, structure_imes);
-  time_info_table[0] += structure_imes[0];
-  time_info_table[1] += structure_imes[1];
-  time_info_table[2] += structure_imes[2];
+
+  this->table_model_->Run(img_list, structure_html_tags, structure_scores,
+                          structure_boxes, structure_times);
+
+  this->time_info_table[0] += structure_times[0];
+  this->time_info_table[1] += structure_times[1];
+  this->time_info_table[2] += structure_times[2];

  std::vector<OCRPredictResult> ocr_result;
  std::string html;
@@ -100,22 +100,22 @@ void PaddleStructure::table(cv::Mat img,

  for (int i = 0; i < img_list.size(); i++) {
    // det
-    this->det(img_list[i], ocr_result, time_info_det);
+    this->det(img_list[i], ocr_result);
    // crop image
    std::vector<cv::Mat> rec_img_list;
    std::vector<int> ocr_box;
    for (int j = 0; j < ocr_result.size(); j++) {
      ocr_box = Utility::xyxyxyxy2xyxy(ocr_result[j].box);
-      ocr_box[0] = max(0, ocr_box[0] - expand_pixel);
-      ocr_box[1] = max(0, ocr_box[1] - expand_pixel),
-      ocr_box[2] = min(img_list[i].cols, ocr_box[2] + expand_pixel);
-      ocr_box[3] = min(img_list[i].rows, ocr_box[3] + expand_pixel);
+      ocr_box[0] = std::max(0, ocr_box[0] - expand_pixel);
+      ocr_box[1] = std::max(0, ocr_box[1] - expand_pixel),
+      ocr_box[2] = std::min(img_list[i].cols, ocr_box[2] + expand_pixel);
+      ocr_box[3] = std::min(img_list[i].rows, ocr_box[3] + expand_pixel);

      cv::Mat crop_img = Utility::crop_image(img_list[i], ocr_box);
      rec_img_list.push_back(crop_img);
    }
    // rec
-    this->rec(rec_img_list, ocr_result, time_info_rec);
+    this->rec(rec_img_list, ocr_result);
    // rebuild table
    html = this->rebuild_table(structure_html_tags[i], structure_boxes[i],
                               ocr_result);
@@ -130,8 +130,8 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
                               std::vector<std::vector<int>> structure_boxes,
                               std::vector<OCRPredictResult> &ocr_result) {
  // match text in same cell
-  std::vector<std::vector<string>> matched(structure_boxes.size(),
-                                           std::vector<std::string>());
+  std::vector<std::vector<std::string>> matched(structure_boxes.size(),
+                                                std::vector<std::string>());

  std::vector<int> ocr_box;
  std::vector<int> structure_box;
@@ -150,7 +150,7 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
        structure_box = structure_boxes[j];
      }
      dis_list[j][0] = this->dis(ocr_box, structure_box);
-      dis_list[j][1] = 1 - this->iou(ocr_box, structure_box);
+      dis_list[j][1] = 1 - Utility::iou(ocr_box, structure_box);
      dis_list[j][2] = j;
    }
    // find min dis idx
@@ -216,28 +216,6 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
  return html_str;
 }

-float PaddleStructure::iou(std::vector<int> &box1, std::vector<int> &box2) {
-  int area1 = max(0, box1[2] - box1[0]) * max(0, box1[3] - box1[1]);
-  int area2 = max(0, box2[2] - box2[0]) * max(0, box2[3] - box2[1]);
-
-  // computing the sum_area
-  int sum_area = area1 + area2;
-
-  // find the each point of intersect rectangle
-  int x1 = max(box1[0], box2[0]);
-  int y1 = max(box1[1], box2[1]);
-  int x2 = min(box1[2], box2[2]);
-  int y2 = min(box1[3], box2[3]);
-
-  // judge if there is an intersect
-  if (y1 >= y2 || x1 >= x2) {
-    return 0.0;
-  } else {
-    int intersect = (x2 - x1) * (y2 - y1);
-    return intersect / (sum_area - intersect + 0.00000001);
-  }
-}
-
 float PaddleStructure::dis(std::vector<int> &box1, std::vector<int> &box2) {
  int x1_1 = box1[0];
  int y1_1 = box1[1];
@@ -253,12 +231,64 @@ float PaddleStructure::dis(std::vector<int> &box1, std::vector<int> &box2) {
      abs(x1_2 - x1_1) + abs(y1_2 - y1_1) + abs(x2_2 - x2_1) + abs(y2_2 - y2_1);
  float dis_2 = abs(x1_2 - x1_1) + abs(y1_2 - y1_1);
  float dis_3 = abs(x2_2 - x2_1) + abs(y2_2 - y2_1);
-  return dis + min(dis_2, dis_3);
+  return dis + std::min(dis_2, dis_3);
+}
+
+void PaddleStructure::reset_timer() {
+  this->time_info_det = {0, 0, 0};
+  this->time_info_rec = {0, 0, 0};
+  this->time_info_cls = {0, 0, 0};
+  this->time_info_table = {0, 0, 0};
+  this->time_info_layout = {0, 0, 0};
+}
+
+void PaddleStructure::benchmark_log(int img_num) {
+  if (this->time_info_det[0] + this->time_info_det[1] + this->time_info_det[2] >
+      0) {
+    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
+                           FLAGS_precision, this->time_info_det, img_num);
+    autolog_det.report();
+  }
+  if (this->time_info_rec[0] + this->time_info_rec[1] + this->time_info_rec[2] >
+      0) {
+    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
+                           this->time_info_rec, img_num);
+    autolog_rec.report();
+  }
+  if (this->time_info_cls[0] + this->time_info_cls[1] + this->time_info_cls[2] >
+      0) {
+    AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                           FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                           this->time_info_cls, img_num);
+    autolog_cls.report();
+  }
+  if (this->time_info_table[0] + this->time_info_table[1] +
+          this->time_info_table[2] >
+      0) {
+    AutoLogger autolog_table("table", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                             FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                             FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                             this->time_info_table, img_num);
+    autolog_table.report();
+  }
+  if (this->time_info_layout[0] + this->time_info_layout[1] +
+          this->time_info_layout[2] >
+      0) {
+    AutoLogger autolog_layout("layout", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                              FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                              FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                              this->time_info_layout, img_num);
+    autolog_layout.report();
+  }
 }

 PaddleStructure::~PaddleStructure() {
-  if (this->recognizer_ != nullptr) {
-    delete this->recognizer_;
+  if (this->table_model_ != nullptr) {
+    delete this->table_model_;
  }
 };

@@ -12,7 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#include <include/clipper.h>
 #include <include/postprocess_op.h>

 namespace PaddleOCR {
@@ -431,7 +430,7 @@ void TablePostProcessor::Run(
      }
    }
    score /= count;
-    if (isnan(score) || rec_boxes.size() == 0) {
+    if (std::isnan(score) || rec_boxes.size() == 0) {
      score = -1;
    }
    rec_scores.push_back(score);
@@ -440,4 +439,137 @@ void TablePostProcessor::Run(
  }
 }

+void PicodetPostProcessor::init(std::string label_path,
+                                const double score_threshold,
+                                const double nms_threshold,
+                                const std::vector<int> &fpn_stride) {
+  this->label_list_ = Utility::ReadDict(label_path);
+  this->score_threshold_ = score_threshold;
+  this->nms_threshold_ = nms_threshold;
+  this->num_class_ = label_list_.size();
+  this->fpn_stride_ = fpn_stride;
+}
+
+void PicodetPostProcessor::Run(std::vector<StructurePredictResult> &results,
+                               std::vector<std::vector<float>> outs,
+                               std::vector<int> ori_shape,
+                               std::vector<int> resize_shape, int reg_max) {
+  int in_h = resize_shape[0];
+  int in_w = resize_shape[1];
+  float scale_factor_h = resize_shape[0] / float(ori_shape[0]);
+  float scale_factor_w = resize_shape[1] / float(ori_shape[1]);
+
+  std::vector<std::vector<StructurePredictResult>> bbox_results;
+  bbox_results.resize(this->num_class_);
+  for (int i = 0; i < this->fpn_stride_.size(); ++i) {
+    int feature_h = std::ceil((float)in_h / this->fpn_stride_[i]);
+    int feature_w = std::ceil((float)in_w / this->fpn_stride_[i]);
+    for (int idx = 0; idx < feature_h * feature_w; idx++) {
+      // score and label
+      float score = 0;
+      int cur_label = 0;
+      for (int label = 0; label < this->num_class_; label++) {
+        if (outs[i][idx * this->num_class_ + label] > score) {
+          score = outs[i][idx * this->num_class_ + label];
+          cur_label = label;
+        }
+      }
+      // bbox
+      if (score > this->score_threshold_) {
+        int row = idx / feature_w;
+        int col = idx % feature_w;
+        std::vector<float> bbox_pred(
+            outs[i + this->fpn_stride_.size()].begin() + idx * 4 * reg_max,
+            outs[i + this->fpn_stride_.size()].begin() +
+                (idx + 1) * 4 * reg_max);
+        bbox_results[cur_label].push_back(
+            this->disPred2Bbox(bbox_pred, cur_label, score, col, row,
+                               this->fpn_stride_[i], resize_shape, reg_max));
+      }
+    }
+  }
+  for (int i = 0; i < bbox_results.size(); i++) {
+    bool flag = bbox_results[i].size() <= 0;
+  }
+  for (int i = 0; i < bbox_results.size(); i++) {
+    bool flag = bbox_results[i].size() <= 0;
+    if (bbox_results[i].size() <= 0) {
+      continue;
+    }
+    this->nms(bbox_results[i], this->nms_threshold_);
+    for (auto box : bbox_results[i]) {
+      box.box[0] = box.box[0] / scale_factor_w;
+      box.box[2] = box.box[2] / scale_factor_w;
+      box.box[1] = box.box[1] / scale_factor_h;
+      box.box[3] = box.box[3] / scale_factor_h;
+      results.push_back(box);
+    }
+  }
+}
+
+StructurePredictResult
+PicodetPostProcessor::disPred2Bbox(std::vector<float> bbox_pred, int label,
+                                   float score, int x, int y, int stride,
+                                   std::vector<int> im_shape, int reg_max) {
+  float ct_x = (x + 0.5) * stride;
+  float ct_y = (y + 0.5) * stride;
+  std::vector<float> dis_pred;
+  dis_pred.resize(4);
+  for (int i = 0; i < 4; i++) {
+    float dis = 0;
+    std::vector<float> bbox_pred_i(bbox_pred.begin() + i * reg_max,
+                                   bbox_pred.begin() + (i + 1) * reg_max);
+    std::vector<float> dis_after_sm =
+        Utility::activation_function_softmax(bbox_pred_i);
+    for (int j = 0; j < reg_max; j++) {
+      dis += j * dis_after_sm[j];
+    }
+    dis *= stride;
+    dis_pred[i] = dis;
+  }
+
+  float xmin = (std::max)(ct_x - dis_pred[0], .0f);
+  float ymin = (std::max)(ct_y - dis_pred[1], .0f);
+  float xmax = (std::min)(ct_x + dis_pred[2], (float)im_shape[1]);
+  float ymax = (std::min)(ct_y + dis_pred[3], (float)im_shape[0]);
+
+  StructurePredictResult result_item;
+  result_item.box = {xmin, ymin, xmax, ymax};
+  result_item.type = this->label_list_[label];
+  result_item.confidence = score;
+
+  return result_item;
+}
+
+void PicodetPostProcessor::nms(std::vector<StructurePredictResult> &input_boxes,
+                               float nms_threshold) {
+  std::sort(input_boxes.begin(), input_boxes.end(),
+            [](StructurePredictResult a, StructurePredictResult b) {
+              return a.confidence > b.confidence;
+            });
+  std::vector<int> picked(input_boxes.size(), 1);
+
+  for (int i = 0; i < input_boxes.size(); ++i) {
+    if (picked[i] == 0) {
+      continue;
+    }
+    for (int j = i + 1; j < input_boxes.size(); ++j) {
+      if (picked[j] == 0) {
+        continue;
+      }
+      float iou = Utility::iou(input_boxes[i].box, input_boxes[j].box);
+      if (iou > nms_threshold) {
+        picked[j] = 0;
+      }
+    }
+  }
+  std::vector<StructurePredictResult> input_boxes_nms;
+  for (int i = 0; i < input_boxes.size(); ++i) {
+    if (picked[i] == 1) {
+      input_boxes_nms.push_back(input_boxes[i]);
+    }
+  }
+  input_boxes = input_boxes_nms;
+}
+
 } // namespace PaddleOCR
@@ -12,21 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/preprocess_op.h>

 namespace PaddleOCR {
@@ -69,13 +54,13 @@ void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
 }

 void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
-                         string limit_type, int limit_side_len, float &ratio_h,
-                         float &ratio_w, bool use_tensorrt) {
+                         std::string limit_type, int limit_side_len,
+                         float &ratio_h, float &ratio_w, bool use_tensorrt) {
  int w = img.cols;
  int h = img.rows;
  float ratio = 1.f;
  if (limit_type == "min") {
-    int min_wh = min(h, w);
+    int min_wh = std::min(h, w);
    if (min_wh < limit_side_len) {
      if (h < w) {
        ratio = float(limit_side_len) / float(h);
@@ -84,7 +69,7 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
      }
    }
  } else {
-    int max_wh = max(h, w);
+    int max_wh = std::max(h, w);
    if (max_wh > limit_side_len) {
      if (h > w) {
        ratio = float(limit_side_len) / float(h);
@@ -97,8 +82,8 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
  int resize_h = int(float(h) * ratio);
  int resize_w = int(float(w) * ratio);

-  resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
-  resize_w = max(int(round(float(resize_w) / 32) * 32), 32);
+  resize_h = std::max(int(round(float(resize_h) / 32) * 32), 32);
+  resize_w = std::max(int(round(float(resize_w) / 32) * 32), 32);

  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
  ratio_h = float(resize_h) / float(h);
@@ -175,4 +160,9 @@ void TablePadImg::Run(const cv::Mat &img, cv::Mat &resize_img,
                     cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
 }

+void Resize::Run(const cv::Mat &img, cv::Mat &resize_img, const int h,
+                 const int w) {
+  cv::resize(img, resize_img, cv::Size(w, h));
+}
+
 } // namespace PaddleOCR
@@ -0,0 +1,149 @@
+// Copyright (c) 2020 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 <include/structure_layout.h>
+
+namespace PaddleOCR {
+
+void StructureLayoutRecognizer::Run(cv::Mat img,
+                                    std::vector<StructurePredictResult> &result,
+                                    std::vector<double> &times) {
+  std::chrono::duration<float> preprocess_diff =
+      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();
+
+  // preprocess
+  auto preprocess_start = std::chrono::steady_clock::now();
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat resize_img;
+  this->resize_op_.Run(srcimg, resize_img, 800, 608);
+  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                          this->is_scale_);
+
+  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
+  this->permute_op_.Run(&resize_img, input.data());
+  auto preprocess_end = std::chrono::steady_clock::now();
+  preprocess_diff += preprocess_end - preprocess_start;
+
+  // inference.
+  auto input_names = this->predictor_->GetInputNames();
+  auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+  auto inference_start = std::chrono::steady_clock::now();
+  input_t->CopyFromCpu(input.data());
+
+  this->predictor_->Run();
+
+  // Get output tensor
+  std::vector<std::vector<float>> out_tensor_list;
+  std::vector<std::vector<int>> output_shape_list;
+  auto output_names = this->predictor_->GetOutputNames();
+  for (int j = 0; j < output_names.size(); j++) {
+    auto output_tensor = this->predictor_->GetOutputHandle(output_names[j]);
+    std::vector<int> output_shape = output_tensor->shape();
+    int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
+                                  std::multiplies<int>());
+    output_shape_list.push_back(output_shape);
+
+    std::vector<float> out_data;
+    out_data.resize(out_num);
+    output_tensor->CopyToCpu(out_data.data());
+    out_tensor_list.push_back(out_data);
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  inference_diff += inference_end - inference_start;
+
+  // postprocess
+  auto postprocess_start = std::chrono::steady_clock::now();
+
+  std::vector<int> bbox_num;
+  int reg_max = 0;
+  for (int i = 0; i < out_tensor_list.size(); i++) {
+    if (i == this->post_processor_.fpn_stride_.size()) {
+      reg_max = output_shape_list[i][2] / 4;
+      break;
+    }
+  }
+  std::vector<int> ori_shape = {srcimg.rows, srcimg.cols};
+  std::vector<int> resize_shape = {resize_img.rows, resize_img.cols};
+  this->post_processor_.Run(result, out_tensor_list, ori_shape, resize_shape,
+                            reg_max);
+  bbox_num.push_back(result.size());
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+  postprocess_diff += postprocess_end - postprocess_start;
+  times.push_back(double(preprocess_diff.count() * 1000));
+  times.push_back(double(inference_diff.count() * 1000));
+  times.push_back(double(postprocess_diff.count() * 1000));
+}
+
+void StructureLayoutRecognizer::LoadModel(const std::string &model_dir) {
+  paddle_infer::Config config;
+  if (Utility::PathExists(model_dir + "/inference.pdmodel") &&
+      Utility::PathExists(model_dir + "/inference.pdiparams")) {
+    config.SetModel(model_dir + "/inference.pdmodel",
+                    model_dir + "/inference.pdiparams");
+  } else if (Utility::PathExists(model_dir + "/model.pdmodel") &&
+             Utility::PathExists(model_dir + "/model.pdiparams")) {
+    config.SetModel(model_dir + "/model.pdmodel",
+                    model_dir + "/model.pdiparams");
+  } else {
+    std::cerr << "[ERROR] not find model.pdiparams or inference.pdiparams in "
+              << model_dir << std::endl;
+    exit(1);
+  }
+
+  if (this->use_gpu_) {
+    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
+    if (this->use_tensorrt_) {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (this->precision_ == "fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      }
+      if (this->precision_ == "int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
+      if (!Utility::PathExists("./trt_layout_shape.txt")) {
+        config.CollectShapeRangeInfo("./trt_layout_shape.txt");
+      } else {
+        config.EnableTunedTensorRtDynamicShape("./trt_layout_shape.txt", true);
+      }
+    }
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+
+  // false for zero copy tensor
+  config.SwitchUseFeedFetchOps(false);
+  // true for multiple input
+  config.SwitchSpecifyInputNames(true);
+
+  config.SwitchIrOptim(true);
+
+  config.EnableMemoryOptim();
+  config.DisableGlogInfo();
+
+  this->predictor_ = paddle_infer::CreatePredictor(config);
+}
+} // namespace PaddleOCR
@@ -34,7 +34,7 @@ void StructureTableRecognizer::Run(
       beg_img_no += this->table_batch_num_) {
    // preprocess
    auto preprocess_start = std::chrono::steady_clock::now();
-    int end_img_no = min(img_num, beg_img_no + this->table_batch_num_);
+    int end_img_no = std::min(img_num, beg_img_no + this->table_batch_num_);
    int batch_num = end_img_no - beg_img_no;
    std::vector<cv::Mat> norm_img_batch;
    std::vector<int> width_list;
@@ -118,7 +118,7 @@ void StructureTableRecognizer::Run(
 }

 void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
-  AnalysisConfig config;
+  paddle_infer::Config config;
  config.SetModel(model_dir + "/inference.pdmodel",
                  model_dir + "/inference.pdiparams");

@@ -133,6 +133,11 @@ void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
        precision = paddle_infer::Config::Precision::kInt8;
      }
      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
+      if (!Utility::PathExists("./trt_table_shape.txt")) {
+        config.CollectShapeRangeInfo("./trt_table_shape.txt");
+      } else {
+        config.EnableTunedTensorRtDynamicShape("./trt_table_shape.txt", true);
+      }
    }
  } else {
    config.DisableGpu();
@@ -152,6 +157,6 @@ void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
  config.EnableMemoryOptim();
  config.DisableGlogInfo();

-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 } // namespace PaddleOCR
@@ -70,6 +70,7 @@ void Utility::VisualizeBboxes(const cv::Mat &srcimg,
                              const std::string &save_path) {
  cv::Mat img_vis;
  srcimg.copyTo(img_vis);
+  img_vis = crop_image(img_vis, structure_result.box);
  for (int n = 0; n < structure_result.cell_box.size(); n++) {
    if (structure_result.cell_box[n].size() == 8) {
      cv::Point rook_points[4];
@@ -280,23 +281,29 @@ void Utility::print_result(const std::vector<OCRPredictResult> &ocr_result) {
  }
 }

-cv::Mat Utility::crop_image(cv::Mat &img, std::vector<int> &area) {
+cv::Mat Utility::crop_image(cv::Mat &img, const std::vector<int> &box) {
  cv::Mat crop_im;
-  int crop_x1 = std::max(0, area[0]);
-  int crop_y1 = std::max(0, area[1]);
-  int crop_x2 = std::min(img.cols - 1, area[2] - 1);
-  int crop_y2 = std::min(img.rows - 1, area[3] - 1);
+  int crop_x1 = std::max(0, box[0]);
+  int crop_y1 = std::max(0, box[1]);
+  int crop_x2 = std::min(img.cols - 1, box[2] - 1);
+  int crop_y2 = std::min(img.rows - 1, box[3] - 1);

-  crop_im = cv::Mat::zeros(area[3] - area[1], area[2] - area[0], 16);
+  crop_im = cv::Mat::zeros(box[3] - box[1], box[2] - box[0], 16);
  cv::Mat crop_im_window =
-      crop_im(cv::Range(crop_y1 - area[1], crop_y2 + 1 - area[1]),
-              cv::Range(crop_x1 - area[0], crop_x2 + 1 - area[0]));
+      crop_im(cv::Range(crop_y1 - box[1], crop_y2 + 1 - box[1]),
+              cv::Range(crop_x1 - box[0], crop_x2 + 1 - box[0]));
  cv::Mat roi_img =
      img(cv::Range(crop_y1, crop_y2 + 1), cv::Range(crop_x1, crop_x2 + 1));
  crop_im_window += roi_img;
  return crop_im;
 }

+cv::Mat Utility::crop_image(cv::Mat &img, const std::vector<float> &box) {
+  std::vector<int> box_int = {(int)box[0], (int)box[1], (int)box[2],
+                              (int)box[3]};
+  return crop_image(img, box_int);
+}
+
 void Utility::sorted_boxes(std::vector<OCRPredictResult> &ocr_result) {
  std::sort(ocr_result.begin(), ocr_result.end(), Utility::comparison_box);
  if (ocr_result.size() > 0) {
@@ -341,4 +348,78 @@ std::vector<int> Utility::xyxyxyxy2xyxy(std::vector<int> &box) {
  return box1;
 }

+float Utility::fast_exp(float x) {
+  union {
+    uint32_t i;
+    float f;
+  } v{};
+  v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
+  return v.f;
+}
+
+std::vector<float>
+Utility::activation_function_softmax(std::vector<float> &src) {
+  int length = src.size();
+  std::vector<float> dst;
+  dst.resize(length);
+  const float alpha = float(*std::max_element(&src[0], &src[0 + length]));
+  float denominator{0};
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] = fast_exp(src[i] - alpha);
+    denominator += dst[i];
+  }
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] /= denominator;
+  }
+  return dst;
+}
+
+float Utility::iou(std::vector<int> &box1, std::vector<int> &box2) {
+  int area1 = std::max(0, box1[2] - box1[0]) * std::max(0, box1[3] - box1[1]);
+  int area2 = std::max(0, box2[2] - box2[0]) * std::max(0, box2[3] - box2[1]);
+
+  // computing the sum_area
+  int sum_area = area1 + area2;
+
+  // find the each point of intersect rectangle
+  int x1 = std::max(box1[0], box2[0]);
+  int y1 = std::max(box1[1], box2[1]);
+  int x2 = std::min(box1[2], box2[2]);
+  int y2 = std::min(box1[3], box2[3]);
+
+  // judge if there is an intersect
+  if (y1 >= y2 || x1 >= x2) {
+    return 0.0;
+  } else {
+    int intersect = (x2 - x1) * (y2 - y1);
+    return intersect / (sum_area - intersect + 0.00000001);
+  }
+}
+
+float Utility::iou(std::vector<float> &box1, std::vector<float> &box2) {
+  float area1 = std::max((float)0.0, box1[2] - box1[0]) *
+                std::max((float)0.0, box1[3] - box1[1]);
+  float area2 = std::max((float)0.0, box2[2] - box2[0]) *
+                std::max((float)0.0, box2[3] - box2[1]);
+
+  // computing the sum_area
+  float sum_area = area1 + area2;
+
+  // find the each point of intersect rectangle
+  float x1 = std::max(box1[0], box2[0]);
+  float y1 = std::max(box1[1], box2[1]);
+  float x2 = std::min(box1[2], box2[2]);
+  float y2 = std::min(box1[3], box2[3]);
+
+  // judge if there is an intersect
+  if (y1 >= y2 || x1 >= x2) {
+    return 0.0;
+  } else {
+    float intersect = (x2 - x1) * (y2 - y1);
+    return intersect / (sum_area - intersect + 0.00000001);
+  }
+}
+
 } // namespace PaddleOCR