Merge branch 'dygraph' of https://github.com/Topdu/PaddleOCR into dygraph

README.md

@@ -121,9 +121,9 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel
     - [Other Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
     - [Other Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
 - Datasets
-    - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
-    - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
-    - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
+    - [General OCR Datasets(Chinese/English)](doc/doc_en/dataset/datasets_en.md)
+    - [HandWritten_OCR_Datasets(Chinese)](doc/doc_en/dataset/handwritten_datasets_en.md)
+    - [Various OCR Datasets(multilingual)](doc/doc_en/dataset/vertical_and_multilingual_datasets_en.md)
 - [Code Structure](./doc/doc_en/tree_en.md)
 - [Visualization](#Visualization)
 - [Community](#Community)

README_ch.md

@@ -128,12 +128,12 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
     - [其它数据标注工具](./doc/doc_ch/data_annotation.md)
     - [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
 - 数据集
-    - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
-    - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
-    - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
-    - [版面分析数据集](./doc/doc_ch/layout_datasets.md)
-    - [表格识别数据集](./doc/doc_ch/table_datasets.md)
-    - [DocVQA数据集](./doc/doc_ch/docvqa_datasets.md)
+    - [通用中英文OCR数据集](doc/doc_ch/dataset/datasets.md)
+    - [手写中文OCR数据集](doc/doc_ch/dataset/handwritten_datasets.md)
+    - [垂类多语言OCR数据集](doc/doc_ch/dataset/vertical_and_multilingual_datasets.md)
+    - [版面分析数据集](doc/doc_ch/dataset/layout_datasets.md)
+    - [表格识别数据集](doc/doc_ch/dataset/table_datasets.md)
+    - [DocVQA数据集](doc/doc_ch/dataset/docvqa_datasets.md)
 - [代码组织结构](./doc/doc_ch/tree.md)
 - [效果展示](#效果展示)
 - [《动手学OCR》电子书📚](./doc/doc_ch/ocr_book.md)

configs/cls/ch_PP-OCRv3/ch_PP-OCRv3_rotnet.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 100
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_ppocr_v3_rotnet
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: cls
+  algorithm: CLS
+  Transform: null
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+    last_conv_stride: [1, 2]
+    last_pool_type: avg
+  Neck:
+  Head:
+    name: ClsHead
+    class_dim: 4
+
+Loss:
+  name: ClsLoss
+  main_indicator: acc
+
+PostProcess:
+  name: ClsPostProcess
+
+Metric:
+  name: ClsMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug:
+        use_tia: False
+    - RandAugment:
+    - SSLRotateResize:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys: ["image", "label"]
+  loader:
+    collate_fn: "SSLRotateCollate"
+    shuffle: true
+    batch_size_per_card: 32
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - SSLRotateResize:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys: ["image", "label"]
+  loader:
+    collate_fn: "SSLRotateCollate"
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 64
+    num_workers: 8
+profiler_options: null

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/ch_PP-OCR_v3_det/
+  save_epoch_step: 100
+  eval_batch_step:
+  - 0
+  - 400
+  cal_metric_during_train: false
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+  distributed: true
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  model_type: det
+  Models:
+    Student:
+      model_type: det
+      algorithm: DB
+      Transform: null
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: true
+      Neck:
+        name: RSEFPN
+        out_channels: 96
+        shortcut: True
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      model_type: det
+      algorithm: DB
+      Transform: null
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: true
+      Neck:
+        name: RSEFPN
+        out_channels: 96
+        shortcut: True
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: ResNet
+        in_channels: 3
+        layers: 50
+      Neck:
+        name: LKPAN
+        out_channels: 256
+      Head:
+        name: DBHead
+        kernel_list: [7,2,2]
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      - ["Student2", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      # act: None
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      # name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: L2
+    factor: 5.0e-05
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - CopyPaste:
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 960
+        - 960
+        max_tries: 50
+        keep_ratio: true
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 8
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest: null
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/ch_PP-OCR_V3_det/
+  save_epoch_step: 100
+  eval_batch_step:
+  - 0
+  - 400
+  cal_metric_during_train: false
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+  distributed: true
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+    disable_se: True
+  Neck:
+    name: RSEFPN
+    out_channels: 96
+    shortcut: True
+  Head:
+    name: DBHead
+    k: 50
+
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: L2
+    factor: 5.0e-05
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 960
+        - 960
+        max_tries: 50
+        keep_ratio: true
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 8
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest: null
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2

deploy/README.md

@@ -22,9 +22,11 @@ PP-OCR has supported muti deployment schemes. Click the link to get the specific
 
 - [Python Inference](../doc/doc_en/inference_ppocr_en.md)
 - [C++ Inference](./cpp_infer/readme.md)
-- [Serving](./pdserving/README.md)
-- [Paddle-Lite](./lite/readme.md)
+- [Serving (Python/C++)](./pdserving/README.md)
+- [Paddle-Lite (ARM CPU/OpenCL ARM GPU/Metal ARM GPU)](./lite/readme.md)
 - [Paddle.js](./paddlejs/README.md)
+- [Jetson Inference]()
+- [XPU Inference]()
 - [Paddle2ONNX](./paddle2onnx/readme.md)
 
 If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
\ No newline at end of file

deploy/README_ch.md

@@ -22,9 +22,11 @@ PP-OCR模型已打通多种场景部署方案，点击链接获取具体的使
 
 - [Python 推理](../doc/doc_ch/inference_ppocr.md)
 - [C++ 推理](./cpp_infer/readme_ch.md)
-- [Serving 服务化部署](./pdserving/README_CN.md)
-- [Paddle-Lite 端侧部署](./lite/readme_ch.md)
-- [Paddle.js 服务化部署](./paddlejs/README_ch.md)
+- [Serving 服务化部署（Python/C++）](./pdserving/README_CN.md)
+- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU/Metal ARM GPU）](./lite/readme_ch.md)
+- [Paddle.js 部署](./paddlejs/README_ch.md)
+- [Jetson 推理]()
+- [XPU 推理]()
 - [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
 
 需要PP-OCR以外的学术算法模型的推理部署，请直接进入相应算法主页面，[入口](../doc/doc_ch/algorithm_overview.md)。
\ No newline at end of file

deploy/slim/quantization/export_model.py

@@ -35,17 +35,7 @@ from ppocr.metrics import build_metric
 import tools.program as program
 from paddleslim.dygraph.quant import QAT
 from ppocr.data import build_dataloader
-
-
-def export_single_model(quanter, model, infer_shape, save_path, logger):
-    quanter.save_quantized_model(
-        model,
-        save_path,
-        input_spec=[
-            paddle.static.InputSpec(
-                shape=[None] + infer_shape, dtype='float32')
-        ])
-    logger.info('inference QAT model is saved to {}'.format(save_path))
+from tools.export_model import export_single_model
 
 
 def main():
@@ -84,17 +74,54 @@ def main():
                                             config['Global'])
 
     # build model
-    # for rec algorithm
     if hasattr(post_process_class, 'character'):
         char_num = len(getattr(post_process_class, 'character'))
         if config['Architecture']["algorithm"] in ["Distillation",
                                                    ]:  # distillation model
             for key in config['Architecture']["Models"]:
+                if config['Architecture']['Models'][key]['Head'][
+                        'name'] == 'MultiHead':  # for multi head
+                    if config['PostProcess'][
+                            'name'] == 'DistillationSARLabelDecode':
+                        char_num = char_num - 2
+                    # update SARLoss params
+                    assert list(config['Loss']['loss_config_list'][-1].keys())[
+                        0] == 'DistillationSARLoss'
+                    config['Loss']['loss_config_list'][-1][
+                        'DistillationSARLoss']['ignore_index'] = char_num + 1
+                    out_channels_list = {}
+                    out_channels_list['CTCLabelDecode'] = char_num
+                    out_channels_list['SARLabelDecode'] = char_num + 2
+                    config['Architecture']['Models'][key]['Head'][
+                        'out_channels_list'] = out_channels_list
+                else:
                     config['Architecture']["Models"][key]["Head"][
                         'out_channels'] = char_num
+        elif config['Architecture']['Head'][
+                'name'] == 'MultiHead':  # for multi head
+            if config['PostProcess']['name'] == 'SARLabelDecode':
+                char_num = char_num - 2
+            # update SARLoss params
+            assert list(config['Loss']['loss_config_list'][1].keys())[
+                0] == 'SARLoss'
+            if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
+                config['Loss']['loss_config_list'][1]['SARLoss'] = {
+                    'ignore_index': char_num + 1
+                }
+            else:
+                config['Loss']['loss_config_list'][1]['SARLoss'][
+                    'ignore_index'] = char_num + 1
+            out_channels_list = {}
+            out_channels_list['CTCLabelDecode'] = char_num
+            out_channels_list['SARLabelDecode'] = char_num + 2
+            config['Architecture']['Head'][
+                'out_channels_list'] = out_channels_list
         else:  # base rec model
             config['Architecture']["Head"]['out_channels'] = char_num
 
+        if config['PostProcess']['name'] == 'SARLabelDecode':  # for SAR model
+            config['Loss']['ignore_index'] = char_num - 1
+
     model = build_model(config['Architecture'])
 
     # get QAT model
@@ -120,21 +147,22 @@ def main():
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))
 
-    infer_shape = [3, 32, 100] if model_type == "rec" else [3, 640, 640]
-
     save_path = config["Global"]["save_inference_dir"]
 
     arch_config = config["Architecture"]
+
+    arch_config = config["Architecture"]
+
     if arch_config["algorithm"] in ["Distillation", ]:  # distillation model
+        archs = list(arch_config["Models"].values())
         for idx, name in enumerate(model.model_name_list):
             model.model_list[idx].eval()
             sub_model_save_path = os.path.join(save_path, name, "inference")
-            export_single_model(quanter, model.model_list[idx], infer_shape,
-                                sub_model_save_path, logger)
+            export_single_model(model.model_list[idx], archs[idx],
+                                sub_model_save_path, logger, quanter)
     else:
         save_path = os.path.join(save_path, "inference")
-        model.eval()
-        export_single_model(quanter, model, infer_shape, save_path, logger)
+        export_single_model(model, arch_config, save_path, logger, quanter)
 
 
 if __name__ == "__main__":

deploy/slim/quantization/quant.py

@@ -112,10 +112,48 @@ def main(config, device, logger, vdl_writer):
         if config['Architecture']["algorithm"] in ["Distillation",
                                                    ]:  # distillation model
             for key in config['Architecture']["Models"]:
+                if config['Architecture']['Models'][key]['Head'][
+                        'name'] == 'MultiHead':  # for multi head
+                    if config['PostProcess'][
+                            'name'] == 'DistillationSARLabelDecode':
+                        char_num = char_num - 2
+                    # update SARLoss params
+                    assert list(config['Loss']['loss_config_list'][-1].keys())[
+                        0] == 'DistillationSARLoss'
+                    config['Loss']['loss_config_list'][-1][
+                        'DistillationSARLoss']['ignore_index'] = char_num + 1
+                    out_channels_list = {}
+                    out_channels_list['CTCLabelDecode'] = char_num
+                    out_channels_list['SARLabelDecode'] = char_num + 2
+                    config['Architecture']['Models'][key]['Head'][
+                        'out_channels_list'] = out_channels_list
+                else:
                     config['Architecture']["Models"][key]["Head"][
                         'out_channels'] = char_num
+        elif config['Architecture']['Head'][
+                'name'] == 'MultiHead':  # for multi head
+            if config['PostProcess']['name'] == 'SARLabelDecode':
+                char_num = char_num - 2
+            # update SARLoss params
+            assert list(config['Loss']['loss_config_list'][1].keys())[
+                0] == 'SARLoss'
+            if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
+                config['Loss']['loss_config_list'][1]['SARLoss'] = {
+                    'ignore_index': char_num + 1
+                }
+            else:
+                config['Loss']['loss_config_list'][1]['SARLoss'][
+                    'ignore_index'] = char_num + 1
+            out_channels_list = {}
+            out_channels_list['CTCLabelDecode'] = char_num
+            out_channels_list['SARLabelDecode'] = char_num + 2
+            config['Architecture']['Head'][
+                'out_channels_list'] = out_channels_list
         else:  # base rec model
             config['Architecture']["Head"]['out_channels'] = char_num
+
+        if config['PostProcess']['name'] == 'SARLabelDecode':  # for SAR model
+            config['Loss']['ignore_index'] = char_num - 1
     model = build_model(config['Architecture'])
 
     pre_best_model_dict = dict()

doc/doc_ch/algorithm_det_db.md

@@ -25,8 +25,8 @@
 
 |模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- | --- |
-|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
-|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_det_fcenet.md

+# FCENet
+
+- [1. 算法简介](#1)
+- [2. 环境配置](#2)
+- [3. 模型训练、评估、预测](#3)
+    - [3.1 训练](#3-1)
+    - [3.2 评估](#3-2)
+    - [3.3 预测](#3-3)
+- [4. 推理部署](#4)
+    - [4.1 Python推理](#4-1)
+    - [4.2 C++推理](#4-2)
+    - [4.3 Serving服务化部署](#4-3)
+    - [4.4 更多推理部署](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. 算法简介
+
+论文信息：
+> [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://arxiv.org/abs/2104.10442)
+> Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang
+> CVPR, 2021
+
+在CTW1500文本检测公开数据集上，算法复现效果如下：
+
+| 模型  |骨干网络|配置文件|precision|recall|Hmean|下载链接|
+|-----| --- | --- | --- | --- | --- | --- |
+| FCE | ResNet50_dcn | [configs/det/det_r50_vd_dcn_fce_ctw.yml](../../configs/det/det_r50_vd_dcn_fce_ctw.yml)| 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)|
+
+<a name="2"></a>
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+<a name="3"></a>
+## 3. 模型训练、评估、预测
+
+上述FCE模型使用CTW1500文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。
+
+数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
+
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 4.1 Python推理
+首先将FCE文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd_dcn骨干网络，在CTW1500英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) )，可以使用如下命令进行转换：
+
+```shell
+python3 tools/export_model.py -c configs/det/det_r50_vd_dcn_fce_ctw.yml -o Global.pretrained_model=./det_r50_dcn_fce_ctw_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_fce
+```
+
+FCE文本检测模型推理，执行非弯曲文本检测，可以执行如下命令：
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=quad
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+![](../imgs_results/det_res_img_10_fce.jpg)
+
+如果想执行弯曲文本检测，可以执行如下命令：
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=poly
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+![](../imgs_results/det_res_img623_fce.jpg)
+
+**注意**：由于CTW1500数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。
+
+<a name="4-2"></a>
+### 4.2 C++推理
+
+由于后处理暂未使用CPP编写，FCE文本检测模型暂不支持CPP推理。
+
+<a name="4-3"></a>
+### 4.3 Serving服务化部署
+
+暂未支持
+
+<a name="4-4"></a>
+### 4.4 更多推理部署
+
+暂未支持
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## 引用
+
+```bibtex
+@InProceedings{zhu2021fourier,
+  title={Fourier Contour Embedding for Arbitrary-Shaped Text Detection},
+  author={Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang},
+  year={2021},
+  booktitle = {CVPR}
+}
+```

doc/doc_ch/algorithm_det_psenet.md

+# PSENet
+
+- [1. 算法简介](#1)
+- [2. 环境配置](#2)
+- [3. 模型训练、评估、预测](#3)
+    - [3.1 训练](#3-1)
+    - [3.2 评估](#3-2)
+    - [3.3 预测](#3-3)
+- [4. 推理部署](#4)
+    - [4.1 Python推理](#4-1)
+    - [4.2 C++推理](#4-2)
+    - [4.3 Serving服务化部署](#4-3)
+    - [4.4 更多推理部署](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. 算法简介
+
+论文信息：
+> [Shape robust text detection with progressive scale expansion network](https://arxiv.org/abs/1903.12473)
+> Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai
+> CVPR, 2019
+
+在ICDAR2015文本检测公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- | --- |
+|PSE| ResNet50_vd | [configs/det/det_r50_vd_pse.yml](../../configs/det/det_r50_vd_pse.yml)| 85.81%    |79.53%|82.55%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE| MobileNetV3| [configs/det/det_mv3_pse.yml](../../configs/det/det_mv3_pse.yml) | 82.20%    |70.48%|75.89%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+
+<a name="2"></a>
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+<a name="3"></a>
+## 3. 模型训练、评估、预测
+
+上述PSE模型使用ICDAR2015文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。
+
+数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
+
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 4.1 Python推理
+首先将PSE文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar) )，可以使用如下命令进行转换：
+
+```shell
+python3 tools/export_model.py -c configs/det/det_r50_vd_pse.yml -o Global.pretrained_model=./det_r50_vd_pse_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_pse
+```
+
+PSE文本检测模型推理，执行非弯曲文本检测，可以执行如下命令：
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=quad
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+![](../imgs_results/det_res_img_10_pse.jpg)
+
+如果想执行弯曲文本检测，可以执行如下命令：
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=poly
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+![](../imgs_results/det_res_img_10_pse_poly.jpg)
+
+**注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文或弯曲文本图像检测效果会比较差。
+
+<a name="4-2"></a>
+### 4.2 C++推理
+
+由于后处理暂未使用CPP编写，PSE文本检测模型暂不支持CPP推理。
+
+<a name="4-3"></a>
+### 4.3 Serving服务化部署
+
+暂未支持
+
+<a name="4-4"></a>
+### 4.4 更多推理部署
+
+暂未支持
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## 引用
+
+```bibtex
+@inproceedings{wang2019shape,
+  title={Shape robust text detection with progressive scale expansion network},
+  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={9336--9345},
+  year={2019}
+}
+```

doc/doc_ch/datasets.md → doc/doc_ch/dataset/datasets.md

@@ -6,17 +6,17 @@
 - [中文文档文字识别](#中文文档文字识别)
 - [ICDAR2019-ArT](#ICDAR2019-ArT)
 
-除了开源数据，用户还可使用合成工具自行合成，可参考[数据合成工具](./data_synthesis.md)；
+除了开源数据，用户还可使用合成工具自行合成，可参考[数据合成工具](../data_synthesis.md)；
 
-如果需要标注自己的数据，可参考[数据标注工具](./data_annotation.md)。  
+如果需要标注自己的数据，可参考[数据标注工具](../data_annotation.md)。  
 
 <a name="ICDAR2019-LSVT"></a>
 #### 1、ICDAR2019-LSVT
 - **数据来源**：https://ai.baidu.com/broad/introduction?dataset=lsvt
 - **数据简介**： 共45w中文街景图像，包含5w（2w测试+3w训练）全标注数据（文本坐标+文本内容），40w弱标注数据（仅文本内容），如下图所示：  
-    ![](../datasets/LSVT_1.jpg)  
+    ![](../../datasets/LSVT_1.jpg)  
     (a) 全标注数据  
-    ![](../datasets/LSVT_2.jpg)  
+    ![](../../datasets/LSVT_2.jpg)  
     (b) 弱标注数据  
 - **下载地址**：https://ai.baidu.com/broad/download?dataset=lsvt
 - **说明**：其中，test数据集的label目前没有开源，如要评估结果，可以去官网提交：https://rrc.cvc.uab.es/?ch=16
@@ -25,16 +25,16 @@
 #### 2、ICDAR2017-RCTW-17
 - **数据来源**：https://rctw.vlrlab.net/
 - **数据简介**：共包含12,000+图像，大部分图片是通过手机摄像头在野外采集的。有些是截图。这些图片展示了各种各样的场景，包括街景、海报、菜单、室内场景和手机应用程序的截图。
-    ![](../datasets/rctw.jpg)
+    ![](../../datasets/rctw.jpg)
 - **下载地址**：https://rctw.vlrlab.net/dataset/
 
 <a name="中文街景文字识别"></a>
 #### 3、中文街景文字识别
 - **数据来源**：https://aistudio.baidu.com/aistudio/competition/detail/8
 - **数据简介**：ICDAR2019-LSVT行识别任务，共包括29万张图片，其中21万张图片作为训练集（带标注），8万张作为测试集（无标注）。数据集采自中国街景，并由街景图片中的文字行区域（例如店铺标牌、地标等等）截取出来而形成。所有图像都经过一些预处理，将文字区域利用仿射变化，等比映射为一张高为48像素的图片，如图所示：  
-    ![](../datasets/ch_street_rec_1.png)  
+    ![](../../datasets/ch_street_rec_1.png)  
     (a) 标注：魅派集成吊顶  
-    ![](../datasets/ch_street_rec_2.png)  
+    ![](../../datasets/ch_street_rec_2.png)  
     (b) 标注：母婴用品连锁  
 - **下载地址**
 https://aistudio.baidu.com/aistudio/datasetdetail/8429
@@ -48,15 +48,15 @@ https://aistudio.baidu.com/aistudio/datasetdetail/8429
     - 包含汉字、英文字母、数字和标点共5990个字符（字符集合：https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt ）
     - 每个样本固定10个字符，字符随机截取自语料库中的句子
     - 图片分辨率统一为280x32  
-    ![](../datasets/ch_doc1.jpg)  
-    ![](../datasets/ch_doc3.jpg)  
+    ![](../../datasets/ch_doc1.jpg)  
+    ![](../../datasets/ch_doc3.jpg)  
 - **下载地址**：https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (密码：lu7m)
 
 <a name="ICDAR2019-ArT"></a>
 #### 5、ICDAR2019-ArT
 - **数据来源**：https://ai.baidu.com/broad/introduction?dataset=art
 - **数据简介**：共包含10,166张图像，训练集5603图，测试集4563图。由Total-Text、SCUT-CTW1500、Baidu Curved Scene Text (ICDAR2019-LSVT部分弯曲数据) 三部分组成，包含水平、多方向和弯曲等多种形状的文本。
-    ![](../datasets/ArT.jpg)
+    ![](../../datasets/ArT.jpg)
 - **下载地址**：https://ai.baidu.com/broad/download?dataset=art
 
 ## 参考文献

doc/doc_ch/handwritten_datasets.md → doc/doc_ch/dataset/handwritten_datasets.md

@@ -9,7 +9,7 @@
 - **数据简介**：
     * 包含在线和离线两类手写数据，`HWDB1.0~1.2`总共有3895135个手写单字样本，分属7356类（7185个汉字和171个英文字母、数字、符号）；`HWDB2.0~2.2`总共有5091页图像，分割为52230个文本行和1349414个文字。所有文字和文本样本均存为灰度图像。部分单字样本图片如下所示。
 
-        ![](../datasets/CASIA_0.jpg)
+        ![](../../datasets/CASIA_0.jpg)
 
 - **下载地址**：http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html
 - **使用建议**：数据为单字，白色背景，可以大量合成文字行进行训练。白色背景可以处理成透明状态，方便添加各种背景。对于需要语义的情况，建议从真实语料出发，抽取单字组成文字行
@@ -22,7 +22,7 @@
 
 - **数据简介**: NIST19数据集适用于手写文档和字符识别的模型训练，从3600位作者的手写样本表格中提取得到，总共包含81万张字符图片。其中9张图片示例如下。
 
-    ![](../datasets/nist_demo.png)
+    ![](../../datasets/nist_demo.png)
 
 
 - **下载地址**: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)

doc/doc_ch/layout_datasets.md → doc/doc_ch/dataset/layout_datasets.md

@@ -27,7 +27,7 @@
 
 #### 2、CDLA数据集
 - **数据来源**：https://github.com/buptlihang/CDLA
-- **数据简介**：publaynet数据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
+- **数据简介**：CDLA据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
 
 <div align="center">
     <img src="../datasets/CDLA_demo/val_0633.jpg" width="500">