Merge branch 'dygraph' into fix_dyg_bugs

paddleocr.py

@@ -29,16 +29,19 @@ from ppocr.utils.logging import get_logger
 logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
 from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
-from tools.infer.utility import draw_ocr, init_args, str2bool
+from tools.infer.utility import draw_ocr, str2bool
+from ppstructure.utility import init_args, draw_structure_result
+from ppstructure.predict_system import OCRSystem, save_structure_res
 
-__all__ = ['PaddleOCR']
+__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res']
 
 model_urls = {
     'det': {
         'ch':
             'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
         'en':
-            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
+        'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
     },
     'rec': {
         'ch': {
@@ -110,14 +113,21 @@ model_urls = {
             'url':
                 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
+        },
+        'structure': {
+            'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
+            'dict_path': 'ppocr/utils/dict/table_dict.txt'
         }
     },
-    'cls':
-        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
+    'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
+    'table': {
+        'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
+        'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
+    }
 }
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.1'
+VERSION = '2.2'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
@@ -129,9 +139,10 @@ def parse_args(mMain=True):
     parser.add_argument("--lang", type=str, default='ch')
     parser.add_argument("--det", type=str2bool, default=True)
     parser.add_argument("--rec", type=str2bool, default=True)
+    parser.add_argument("--type", type=str, default='ocr')
 
     for action in parser._actions:
-        if action.dest == 'rec_char_dict_path':
+        if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
             action.default = None
     if mMain:
         return parser.parse_args()
@@ -142,6 +153,42 @@ def parse_args(mMain=True):
         return argparse.Namespace(**inference_args_dict)
 
 
+def parse_lang(lang):
+    latin_lang = [
+        'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
+        'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
+        'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
+        'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
+    ]
+    arabic_lang = ['ar', 'fa', 'ug', 'ur']
+    cyrillic_lang = [
+        'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
+        'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
+    ]
+    devanagari_lang = [
+        'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
+        'gom', 'sa', 'bgc'
+    ]
+    if lang in latin_lang:
+        lang = "latin"
+    elif lang in arabic_lang:
+        lang = "arabic"
+    elif lang in cyrillic_lang:
+        lang = "cyrillic"
+    elif lang in devanagari_lang:
+        lang = "devanagari"
+    assert lang in model_urls[
+        'rec'], 'param lang must in {}, but got {}'.format(
+        model_urls['rec'].keys(), lang)
+    if lang == "ch":
+        det_lang = "ch"
+    elif lang == 'structure':
+        det_lang = 'structure'
+    else:
+        det_lang = "en"
+    return lang, det_lang
+
+
 class PaddleOCR(predict_system.TextSystem):
     def __init__(self, **kwargs):
         """
@@ -154,52 +201,17 @@ class PaddleOCR(predict_system.TextSystem):
         if not params.show_log:
             logger.setLevel(logging.INFO)
         self.use_angle_cls = params.use_angle_cls
-        lang = params.lang
-        latin_lang = [
-            'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
-            'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
-            'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
-            'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
-        ]
-        arabic_lang = ['ar', 'fa', 'ug', 'ur']
-        cyrillic_lang = [
-            'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
-            'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
-        ]
-        devanagari_lang = [
-            'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
-            'gom', 'sa', 'bgc'
-        ]
-        if lang in latin_lang:
-            lang = "latin"
-        elif lang in arabic_lang:
-            lang = "arabic"
-        elif lang in cyrillic_lang:
-            lang = "cyrillic"
-        elif lang in devanagari_lang:
-            lang = "devanagari"
-        assert lang in model_urls[
-            'rec'], 'param lang must in {}, but got {}'.format(
-            model_urls['rec'].keys(), lang)
-        if lang == "ch":
-            det_lang = "ch"
-        else:
-            det_lang = "en"
-        use_inner_dict = False
-        if params.rec_char_dict_path is None:
-            use_inner_dict = True
-            params.rec_char_dict_path = model_urls['rec'][lang][
-                'dict_path']
+        lang, det_lang = parse_lang(params.lang)
 
         # init model dir
         params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
-                                                              os.path.join(BASE_DIR, VERSION, 'det', det_lang),
+                                                              os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
                                                               model_urls['det'][det_lang])
         params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
-                                                              os.path.join(BASE_DIR, VERSION, 'rec', lang),
+                                                              os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
                                                               model_urls['rec'][lang]['url'])
         params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
-                                                              os.path.join(BASE_DIR, VERSION, 'cls'),
+                                                              os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
                                                               model_urls['cls'])
         # download model
         maybe_download(params.det_model_dir, det_url)
@@ -212,9 +224,9 @@ class PaddleOCR(predict_system.TextSystem):
         if params.rec_algorithm not in SUPPORT_REC_MODEL:
             logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
             sys.exit(0)
-        if use_inner_dict:
-            params.rec_char_dict_path = str(
-                Path(__file__).parent / params.rec_char_dict_path)
+
+        if params.rec_char_dict_path is None:
+            params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
 
         print(params)
         # init det_model and rec_model
@@ -272,6 +284,59 @@ class PaddleOCR(predict_system.TextSystem):
             return rec_res
 
 
+class PPStructure(OCRSystem):
+    def __init__(self, **kwargs):
+        params = parse_args(mMain=False)
+        params.__dict__.update(**kwargs)
+        if not params.show_log:
+            logger.setLevel(logging.INFO)
+        lang, det_lang = parse_lang(params.lang)
+
+        # init model dir
+        params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
+                                                              os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
+                                                              model_urls['det'][det_lang])
+        params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
+                                                              os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
+                                                              model_urls['rec'][lang]['url'])
+        params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
+                                                                  os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
+                                                                  model_urls['table']['url'])
+        # download model
+        maybe_download(params.det_model_dir, det_url)
+        maybe_download(params.rec_model_dir, rec_url)
+        maybe_download(params.table_model_dir, table_url)
+
+        if params.rec_char_dict_path is None:
+            params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
+        if params.table_char_dict_path is None:
+            params.table_char_dict_path = str(Path(__file__).parent / model_urls['table']['dict_path'])
+
+        print(params)
+        super().__init__(params)
+
+    def __call__(self, img):
+        if isinstance(img, str):
+            # download net image
+            if img.startswith('http'):
+                download_with_progressbar(img, 'tmp.jpg')
+                img = 'tmp.jpg'
+            image_file = img
+            img, flag = check_and_read_gif(image_file)
+            if not flag:
+                with open(image_file, 'rb') as f:
+                    np_arr = np.frombuffer(f.read(), dtype=np.uint8)
+                    img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+            if img is None:
+                logger.error("error in loading image:{}".format(image_file))
+                return None
+        if isinstance(img, np.ndarray) and len(img.shape) == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        res = super().__call__(img)
+        return res
+
+
 def main():
     # for cmd
     args = parse_args(mMain=True)
@@ -284,14 +349,28 @@ def main():
     if len(image_file_list) == 0:
         logger.error('no images find in {}'.format(args.image_dir))
         return
+    if args.type == 'ocr':
+        engine = PaddleOCR(**(args.__dict__))
+    elif args.type == 'structure':
+        engine = PPStructure(**(args.__dict__))
+    else:
+        raise NotImplementedError
 
-    ocr_engine = PaddleOCR(**(args.__dict__))
     for img_path in image_file_list:
+        img_name = os.path.basename(img_path).split('.')[0]
         logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
-        result = ocr_engine.ocr(img_path,
+        if args.type == 'ocr':
+            result = engine.ocr(img_path,
                                 det=args.det,
                                 rec=args.rec,
                                 cls=args.use_angle_cls)
-        if result is not None:
-            for line in result:
-                logger.info(line)
+            if result is not None:
+                for line in result:
+                    logger.info(line)
+        elif args.type == 'structure':
+            result = engine(img_path)
+            save_structure_res(result, args.output, img_name)
+
+            for item in result:
+                item.pop('img')
+                logger.info(item['res'])
\ No newline at end of file

ppocr/data/imaug/label_ops.py

@@ -383,14 +383,15 @@ class TableLabelEncode(object):
         list_elem = []
         with open(character_dict_path, "rb") as fin:
             lines = fin.readlines()
-            substr = lines[0].decode('utf-8').strip("\n").split("\t")
+            substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
             character_num = int(substr[0])
             elem_num = int(substr[1])
-            for cno in range(1, 1 + character_num):
-                character = lines[cno].decode('utf-8').strip("\n")
+
+            for cno in range(1, 1+character_num):
+                character = lines[cno].decode('utf-8').strip("\r\n")
                 list_character.append(character)
-            for eno in range(1 + character_num, 1 + character_num + elem_num):
-                elem = lines[eno].decode('utf-8').strip("\n")
+            for eno in range(1+character_num, 1+character_num+elem_num):
+                elem = lines[eno].decode('utf-8').strip("\r\n")
                 list_elem.append(elem)
         return list_character, list_elem
 

ppocr/data/simple_dataset.py

@@ -14,7 +14,6 @@
 import numpy as np
 import os
 import random
-import traceback
 from paddle.io import Dataset
 
 from .imaug import transform, create_operators
@@ -46,7 +45,6 @@ class SimpleDataSet(Dataset):
         self.seed = seed
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
         self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
-        self.check_data()
         self.data_idx_order_list = list(range(len(self.data_lines)))
         if self.mode == "train" and self.do_shuffle:
             self.shuffle_data_random()
@@ -103,18 +101,25 @@ class SimpleDataSet(Dataset):
 
     def __getitem__(self, idx):
         file_idx = self.data_idx_order_list[idx]
-        data = self.data_lines[file_idx]
+        data_line = self.data_lines[file_idx]
         try:
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            data = {'img_path': img_path, 'label': label}
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
             with open(data['img_path'], 'rb') as f:
                 img = f.read()
                 data['image'] = img
             data['ext_data'] = self.get_ext_data()
             outs = transform(data, self.ops)
-        except:
-            error_meg = traceback.format_exc()
+        except Exception as e:
             self.logger.error(
-                "When parsing file {} and label {}, error happened with msg: {}".format(
-                    data['img_path'],data['label'], error_meg))
+                "When parsing line {}, error happened with msg: {}".format(
+                    data_line, e))
             outs = None
         if outs is None:
             # during evaluation, we should fix the idx to get same results for many times of evaluation.
@@ -125,17 +130,3 @@ class SimpleDataSet(Dataset):
 
     def __len__(self):
         return len(self.data_idx_order_list)
-
-    def check_data(self):
-        new_data_lines = []
-        for data_line in self.data_lines:
-            data_line = data_line.decode('utf-8')
-            substr = data_line.strip("\n").strip("\r").split(self.delimiter)
-            file_name = substr[0]
-            label = substr[1]
-            img_path = os.path.join(self.data_dir, file_name)
-            if os.path.exists(img_path):
-                new_data_lines.append({'img_path': img_path, 'label': label})
-            else:
-                self.logger.info("{} does not exist!".format(img_path))
-        self.data_lines = new_data_lines
\ No newline at end of file

ppocr/modeling/architectures/distillation_model.py

@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
                 pretrained = model_config.pop("pretrained")
             model = BaseModel(model_config)
             if pretrained is not None:
-                model = load_pretrained_params(model, pretrained)
+                load_pretrained_params(model, pretrained)
             if freeze_params:
                 for param in model.parameters():
                     param.trainable = False

ppocr/postprocess/rec_postprocess.py

@@ -346,14 +346,14 @@ class TableLabelDecode(object):
         list_elem = []
         with open(character_dict_path, "rb") as fin:
             lines = fin.readlines()
-            substr = lines[0].decode('utf-8').strip("\n").split("\t")
+            substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split("\t")
             character_num = int(substr[0])
             elem_num = int(substr[1])
             for cno in range(1, 1 + character_num):
-                character = lines[cno].decode('utf-8').strip("\n")
+                character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
                 list_character.append(character)
             for eno in range(1 + character_num, 1 + character_num + elem_num):
-                elem = lines[eno].decode('utf-8').strip("\n")
+                elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
                 list_elem.append(elem)
         return list_character, list_elem
 

ppocr/utils/gen_label.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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.
 import os
 import argparse
 import json
@@ -31,7 +31,9 @@ def gen_det_label(root_path, input_dir, out_label):
         for label_file in os.listdir(input_dir):
             img_path = root_path + label_file[3:-4] + ".jpg"
             label = []
-            with open(os.path.join(input_dir, label_file), 'r') as f:
+            with open(
+                    os.path.join(input_dir, label_file), 'r',
+                    encoding='utf-8-sig') as f:
                 for line in f.readlines():
                     tmp = line.strip("\n\r").replace("\xef\xbb\xbf",
                                                      "").split(',')

ppstructure/README.md

+English | [简体中文](README_ch.md)
+
+# PP-Structure
+
+PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
+- Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser)
+- Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR)
+- Support to extract excel files from the table areas
+- Support python whl package and command line usage, easy to use
+- Support custom training for layout analysis and table structure tasks
+- The total model size is only about 18.6M (continuous optimization)
+
+## 1. Visualization
+
+<img src="../doc/table/ppstructure.GIF" width="100%"/>
+
+
+
+## 2. Installation
+
+### 2.1 Install requirements
+
+- **（1) Install PaddlePaddle**
+
+```bash
+pip3 install --upgrade pip
+
+# GPU
+python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
+
+# CPU
+ python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
+
+# For more，refer[Installation](https://www.paddlepaddle.org.cn/install/quick)。
+```
+
+- **(2) Install Layout-Parser**
+
+```bash
+pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+```
+
+### 2.2 Install PaddleOCR（including PP-OCR and PP-Structure）
+
+- **（1) PIP install PaddleOCR whl package（inference only）**
+
+```bash
+pip install "paddleocr>=2.2"
+```
+
+- **（2) Clone PaddleOCR（Inference+training）**
+
+```bash
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+
+## 3 Quick Start
+
+### 3.1 Use by command line
+
+```bash
+paddleocr --image_dir=../doc/table/1.png --type=structure
+```
+
+### 3.2 Use by python API
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,draw_structure_result,save_structure_res
+
+table_engine = PPStructure(show_log=True)
+
+save_folder = './output/table'
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
+
+for line in result:
+    line.pop('img')
+    print(line)
+
+from PIL import Image
+
+font_path = '../doc/fonts/simfang.ttf'
+image = Image.open(img_path).convert('RGB')
+im_show = draw_structure_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+### 3.3 Return results
+The return result of PPStructure is a list composed of a dict, an example is as follows
+
+```shell
+[
+  {   'type': 'Text',
+      'bbox': [34, 432, 345, 462],
+      'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
+                [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
+  }
+]
+```
+The description of each field in dict is as follows
+
+| Parameter            | Description           |
+| --------------- | -------------|
+|type|Type of image area|
+|bbox|The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y]|
+|res|OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text|
+
+
+### 3.4 Parameter Description：
+
+| Parameter            | Description                                     | Default value                                        |
+| --------------- | ---------------------------------------- | ------------------------------------------- |
+| output          | The path where excel and recognition results are saved                | ./output/table                              |
+| table_max_len   | The long side of the image is resized in table structure model  | 488                                         |
+| table_model_dir | inference model path of table structure model          | None                                        |
+| table_char_type | dict path of table structure model                 | ../ppocr/utils/dict/table_structure_dict.tx |
+
+Most of the parameters are consistent with the paddleocr whl package, see [doc of whl](../doc/doc_en/whl_en.md)
+
+After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
+
+## 4. PPStructure Pipeline
+
+the process is as follows
+![pipeline](../doc/table/pipeline_en.jpg)
+
+In PPStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will  be converted to an excel file of the same table style via Table OCR.
+
+### 4.1 LayoutParser
+
+Layout analysis divides the document data into regions, including the use of Python scripts for layout analysis tools, extraction of special category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README_en.md).
+
+### 4.2 Table Structure
+
+Table OCR converts table image into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed, please refer to [document](table/README.md)
+
+## 5. Predictive by inference engine
+
+Use the following commands to complete the inference.
+
+```python
+cd PaddleOCR/ppstructure
+
+# download model
+mkdir inference && cd inference
+# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
+# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+# Download the table structure model of the ultra-lightweight Chinese OCR model and uncompress it
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
+cd ..
+
+python3 table/predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
+```
+After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
+
+**Model List**
+
+
+|model name|description|config|model size|download|
+| --- | --- | --- | --- | --- |
+|en_ppocr_mobile_v2.0_table_structure|Table structure prediction for English table scenarios|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |

ppstructure/README_ch.md

+[English](README.md) | 简体中文
+
+# PP-Structure
+
+PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，主要特性如下：
+- 支持对图片形式的文档进行版面分析，可以划分**文字、标题、表格、图片以及列表**5类区域（与Layout-Parser联合使用）
+- 支持文字、标题、图片以及列表区域提取为文字字段（与PP-OCR联合使用）
+- 支持表格区域进行结构化分析，最终结果输出Excel文件
+- 支持python whl包和命令行两种方式，简单易用
+- 支持版面分析和表格结构化两类任务自定义训练
+- 总模型大小仅有18.6M（持续优化）
+
+## 1. 效果展示
+
+<img src="../doc/table/ppstructure.GIF" width="100%"/>
+
+
+
+## 2. 安装
+
+### 2.1 安装依赖
+
+- **（1) 安装PaddlePaddle**
+
+```bash
+pip3 install --upgrade pip
+
+# GPU安装
+python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
+
+# CPU安装
+ python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
+
+# 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
+```
+
+- **(2) 安装 Layout-Parser**
+
+```bash
+pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+```
+
+### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
+
+- **（1) PIP快速安装PaddleOCR whl包（仅预测）**
+
+```bash
+pip install "paddleocr>=2.2" # 推荐使用2.2+版本
+```
+
+- **（2) 完整克隆PaddleOCR源码（预测+训练）**
+
+```bash
+【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
+
+#如果因为网络问题无法pull成功，也可选择使用码云上的托管：
+git clone https://gitee.com/paddlepaddle/PaddleOCR
+
+#注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
+```
+
+
+## 3 PP-Structure 快速开始
+
+### 3.1 命令行使用（默认参数，极简）
+
+```bash
+paddleocr --image_dir=../doc/table/1.png --type=structure
+```
+
+### 3.2 Python脚本使用（自定义参数，灵活）
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,draw_structure_result,save_structure_res
+
+table_engine = PPStructure(show_log=True)
+
+save_folder = './output/table'
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
+
+for line in result:
+    line.pop('img')
+    print(line)
+
+from PIL import Image
+
+font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
+image = Image.open(img_path).convert('RGB')
+im_show = draw_structure_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+### 3.3 返回结果说明
+PaddleStructure 的返回结果为一个dict组成的list，示例如下
+
+```shell
+[
+  {   'type': 'Text',
+      'bbox': [34, 432, 345, 462],
+      'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
+                [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
+  }
+]
+```
+dict 里各个字段说明如下
+
+| 字段            | 说明           |
+| --------------- | -------------|
+|type|图片区域的类型|
+|bbox|图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y]|
+|res|图片区域的OCR或表格识别结果。<br> 表格: 表格的HTML字符串; <br> OCR: 一个包含各个单行文字的检测坐标和识别结果的元组|
+
+
+### 3.4 参数说明
+
+| 字段            | 说明                                     | 默认值                                      |
+| --------------- | ---------------------------------------- | ------------------------------------------- |
+| output          | excel和识别结果保存的地址                | ./output/table                              |
+| table_max_len   | 表格结构模型预测时，图像的长边resize尺度 | 488                                         |
+| table_model_dir | 表格结构模型 inference 模型地址          | None                                        |
+| table_char_type | 表格结构模型所用字典地址                 | ../ppocr/utils/dict/table_structure_dict.tx |
+
+大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
+
+运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
+
+
+## 4. PP-Structure Pipeline介绍
+
+![pipeline](../doc/table/pipeline.jpg)
+
+在PP-Structure中，图片会先经由Layout-Parser进行版面分析，在版面分析中，会对图片里的区域进行分类，包括**文字、标题、图片、列表和表格**5类。对于前4类区域，直接使用PP-OCR完成对应区域文字检测与识别。对于表格类区域，经过表格结构化处理后，表格图片转换为相同表格样式的Excel文件。
+
+### 4.1 版面分析
+
+版面分析对文档数据进行区域分类，其中包括版面分析工具的Python脚本使用、提取指定类别检测框、性能指标以及自定义训练版面分析模型，详细内容可以参考[文档](layout/README_ch.md)。
+
+### 4.2 表格结构化
+
+表格结构化将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)
+
+## 5. 预测引擎推理（与whl包效果相同）
+
+使用如下命令即可完成预测引擎的推理
+
+```python
+cd ppstructure
+
+# 下载模型
+mkdir inference && cd inference
+# 下载超轻量级中文OCR模型的检测模型并解压
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
+# 下载超轻量级中文OCR模型的识别模型并解压
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+# 下载超轻量级英文表格英寸模型并解压
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
+cd ..
+
+python3 table/predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --det_limit_side_len=736 --det_limit_type=min --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
+```
+运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
+
+**Model List**
+
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+| --- | --- | --- | --- | --- |
+|en_ppocr_mobile_v2.0_table_structure|英文表格场景的表格结构预测|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |

ppstructure/layout/README.md

+English | [简体中文](README_ch.md)
+
+
+# Getting Started
+
+[1. Install whl package](#Install whl package)
+
+[2. Quick Start](#Quick Start)
+
+[3. PostProcess](#PostProcess)
+
+[4. Results](#Results)
+
+[5. Training](#Training)
+
+<a name="Install whl package"></a>
+
+## 1.  Install whl package
+```bash
+wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+pip install -U layoutparser-0.0.0-py3-none-any.whl
+```
+
+<a name="Quick Start"></a>
+
+## 2. Quick Start
+
+Use LayoutParser to identify the layout of a given document:
+
+```python
+import cv2
+import layoutparser as lp
+image = cv2.imread("doc/table/layout.jpg")
+image = image[..., ::-1]
+
+# load model
+model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
+                                threshold=0.5,
+                                label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
+                                enforce_cpu=False,
+                                enable_mkldnn=True)
+# detect
+layout = model.detect(image)
+
+# show result
+show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
+show_img.show()
+```
+
+The following figure shows the result, with different colored detection boxes representing different categories and displaying specific categories in the upper left corner of the box with `show_element_type`
+
+<div align="center">
+<img src="../../doc/table/result_all.jpg"  width = "600" />
+</div>
+`PaddleDetectionLayoutModel`parameters are described as follows:
+
+|   parameter    |                       description                        |   default   |                            remark                            |
+| :------------: | :------------------------------------------------------: | :---------: | :----------------------------------------------------------: |
+|  config_path   |                    model config path                     |    None     | Specify config_ path will automatically download the model (only for the first time,the model will exist and will not be downloaded again) |
+|   model_path   |                        model path                        |    None     | local model path, config_ path and model_ path must be set to one, cannot be none at the same time |
+|   threshold    |              threshold of prediction score               |     0.5     |                              \                               |
+|  input_shape   |                 picture size of reshape                  | [3,640,640] |                              \                               |
+|   batch_size   |                    testing batch size                    |      1      |                              \                               |
+|   label_map    |                  category mapping table                  |    None     | Setting config_ path, it can be none, and the label is automatically obtained according to the dataset name_ map |
+|  enforce_cpu   |                    whether to use CPU                    |    False    |      False to use GPU, and True to force the use of CPU      |
+| enforce_mkldnn | whether mkldnn acceleration is enabled in CPU prediction |    True     |                              \                               |
+|   thread_num   |                the number of CPU threads                 |     10      |                              \                               |
+
+The following model configurations and label maps are currently supported, which you can use by modifying '--config_path' and '--label_map' to detect different types of content:
+
+| dataset                                                      | config_path                                                  | label_map                                                 |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
+| [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"}                                               |
+| TableBank latex                                              | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"}                                               |
+| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)        | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config      | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
+
+* TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
+* Download TableBank dataset contains both word and latex。
+
+<a name="PostProcess"></a>
+
+## 3. PostProcess
+
+Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:
+
+```python
+# follow the above code
+# filter areas for a specific text type
+text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
+figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
+
+# text areas may be detected within the image area, delete these areas
+text_blocks = lp.Layout([b for b in text_blocks \
+                   if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
+
+# sort text areas and assign ID
+h, w = image.shape[:2]
+
+left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
+
+left_blocks = text_blocks.filter_by(left_interval, center=True)
+left_blocks.sort(key = lambda b:b.coordinates[1])
+
+right_blocks = [b for b in text_blocks if b not in left_blocks]
+right_blocks.sort(key = lambda b:b.coordinates[1])
+
+# the two lists are merged and the indexes are added in order
+text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
+
+# display result
+show_img = lp.draw_box(image, text_blocks,
+            box_width=3,
+            show_element_id=True)
+show_img.show()
+```
+
+Displays results with only the "Text" category：
+
+<div align="center">
+<img src="../../doc/table/result_text.jpg"  width = "600" />
+</div>
+<a name="Results"></a>
+
+## 4. Results
+
+| Dataset   | mAP  | CPU time cost | GPU time cost |
+| --------- | ---- | ------------- | ------------- |
+| PubLayNet | 93.6 | 1713.7ms      | 66.6ms        |
+| TableBank | 96.2 | 1968.4ms      | 65.1ms        |
+
+**Envrionment：**
+
+​    **CPU：**  Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
+
+​    **GPU：**  a single NVIDIA Tesla P40
+
+<a name="Training"></a>
+
+## 5. Training
+
+The above model is based on PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) ，if you want to train your own layout parser model，please refer to：[train_layoutparser_model](train_layoutparser_model.md)

test1/layout/README.md → ppstructure/layout/README_ch.md

+[English](README.md) | 简体中文
+
 # 版面分析使用说明
 
-* [1. 安装whl包](#安装whl包)
-* [2. 使用](#使用)
-* [3. 后处理](#后处理)
-* [4. 指标](#指标)
-* [5. 训练版面分析模型](#训练版面分析模型)
+[1. 安装whl包](#安装whl包)
+
+[2. 使用](#使用)
+
+[3. 后处理](#后处理)
+
+[4. 指标](#指标)
+
+[5. 训练版面分析模型](#训练版面分析模型)
 
 <a name="安装whl包"></a>
 
 ## 1.  安装whl包
 ```bash
-wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
-pip install -U layoutparser-0.0.0-py3-none-any.whl
+pip install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
 ```
 
 <a name="使用"></a>
@@ -21,21 +26,23 @@ pip install -U layoutparser-0.0.0-py3-none-any.whl
 使用layoutparser识别给定文档的布局：
 
 ```python
+import cv2
 import layoutparser as lp
-image = cv2.imread("imags/paper-image.jpg")
+image = cv2.imread("doc/table/layout.jpg")
 image = image[..., ::-1]
 
 # 加载模型
-model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config", 
+model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
                                 threshold=0.5,
                                 label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
-                                enforce_cpu=False, 
+                                enforce_cpu=False,
                                 enable_mkldnn=True)
 # 检测
 layout = model.detect(image)
 
 # 显示结果
-lp.draw_box(image, layout, box_width=3, show_element_type=True)
+show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
+show_img.show()
 ```
 
 下图展示了结果，不同颜色的检测框表示不同的类别，并通过`show_element_type`在框的左上角显示具体类别：
@@ -67,7 +74,7 @@ lp.draw_box(image, layout, box_width=3, show_element_type=True)
 | [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)        | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config      | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
 
 * TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
-* 下载TableBank数据集同时包含word和latex。
+* 下载的TableBank数据集里同时包含word和latex。
 
 <a name="后处理"></a>
 
@@ -76,6 +83,7 @@ lp.draw_box(image, layout, box_width=3, show_element_type=True)
 版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
 
 ```python
+# 接上面代码
 # 首先过滤特定文本类型的区域
 text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
 figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
@@ -84,7 +92,7 @@ figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
 text_blocks = lp.Layout([b for b in text_blocks \
                    if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
 
-# 对文本区域排序并分配id 
+# 对文本区域排序并分配id
 h, w = image.shape[:2]
 
 left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
@@ -99,9 +107,10 @@ right_blocks.sort(key = lambda b:b.coordinates[1])
 text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
 
 # 显示结果
-lp.draw_box(image, text_blocks,
-            box_width=3, 
+show_img = lp.draw_box(image, text_blocks,
+            box_width=3,
             show_element_id=True)
+show_img.show()
 ```
 
 显示只有"Text"类别的结果：
@@ -119,15 +128,14 @@ lp.draw_box(image, text_blocks,
 | PubLayNet | 93.6 | 1713.7ms      | 66.6ms        |
 | TableBank | 96.2 | 1968.4ms      | 65.1ms        |
 
-**Envrionment：**	
+**Envrionment：**
 
-​	**CPU：**  Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
+​    **CPU：**  Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
 
-​	**GPU：**  a single NVIDIA Tesla P40
+​    **GPU：**  a single NVIDIA Tesla P40
 
 <a name="训练版面分析模型"></a>
 
 ## 5. 训练版面分析模型
 
-上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model.md)
-
+上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model_ch.md)

ppstructure/layout/train_layoutparser_model.md

+# Training layout-parse
+
+[1. Installation](#Installation)
+
+​  [1.1 Requirements](#Requirements)
+
+​  [1.2 Install PaddleDetection](#Install PaddleDetection)
+
+[2.  Data preparation](#Data preparation)
+
+[3. Configuration](#Configuration)
+
+[4. Training](#Training)
+
+[5. Prediction](#Prediction)
+
+[6. Deployment](#Deployment)
+
+​  [6.1 Export model](#Export model)
+
+​  [6.2 Inference](#Inference)  
+
+<a name="Installation"></a>
+
+## 1.  Installation
+
+<a name="Requirements"></a>
+
+### 1.1 Requirements
+
+- PaddlePaddle 2.1
+- OS 64 bit
+- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
+- pip/pip3(9.0.1+), 64 bit
+- CUDA >= 10.1
+- cuDNN >= 7.6
+
+<a name="Install PaddleDetection"></a>
+
+### 1.2 Install PaddleDetection
+
+```bash
+# Clone PaddleDetection repository
+cd <path/to/clone/PaddleDetection>
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+
+cd PaddleDetection
+# Install other dependencies
+pip install -r requirements.txt
+```
+
+For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
+
+<a name="Data preparation"></a>
+
+## 2. Data preparation
+
+Download the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset
+
+```bash
+cd PaddleDetection/dataset/
+mkdir publaynet
+# execute the command，download PubLayNet
+wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
+# unpack
+tar -xvf publaynet.tar.gz
+```
+
+PubLayNet directory structure after decompressing ：
+
+| File or Folder | Description                                      | num     |
+| :------------- | :----------------------------------------------- | ------- |
+| `train/`       | Images in the training subset                    | 335,703 |
+| `val/`         | Images in the validation subset                  | 11,245  |
+| `test/`        | Images in the testing subset                     | 11,405  |
+| `train.json`   | Annotations for training images                  |  1       |
+| `val.json`     | Annotations for validation images                |  1       |
+| `LICENSE.txt`  | Plaintext version of the CDLA-Permissive license |   1      |
+| `README.txt`   | Text file with the file names and description    |   1      |
+
+For other datasets，please refer to [the PrepareDataSet]((https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md) )
+
+<a name="Configuration"></a>
+
+## 3. Configuration
+
+We use the  `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` configuration for training，the configuration file is as follows
+
+```bash
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/ppyolov2_r50vd_dcn.yml',
+  './_base_/optimizer_365e.yml',
+  './_base_/ppyolov2_reader.yml',
+]
+
+snapshot_epoch: 8
+weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
+```
+The `ppyolov2_r50vd_dcn_365e_coco.yml` configuration depends on other configuration files, in this case:
+
+- coco_detection.yml：mainly explains the path of training data and verification data
+
+- runtime.yml：mainly describes the common parameters, such as whether to use the GPU and how many epoch to save model etc.
+
+- optimizer_365e.yml：mainly explains the learning rate and optimizer configuration
+
+- ppyolov2_r50vd_dcn.yml：mainly describes the model and the  network
+
+- ppyolov2_reader.yml：mainly describes the configuration of data readers, such as batch size and number of concurrent loading child processes, and also includes post preprocessing, such as resize and data augmention etc.
+
+
+Modify the preceding files, such as the dataset path and batch size etc.
+
+<a name="Training"></a>
+
+## 4. Training
+
+PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:
+
+* GPU single card training
+
+```bash
+export CUDA_VISIBLE_DEVICES=0 #Don't need to run this command on Windows and Mac
+python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
+```
+
+* GPU multi-card training
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
+```
+
+--eval: training while verifying
+
+* Model recovery training
+
+During the daily training, if training is interrupted due to some reasons, you can use the -r command to resume the training:
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
+```
+
+Note: If you encounter "`Out of memory error`" , try reducing `batch_size` in the `ppyolov2_reader.yml`  file
+
+prediction<a name="Prediction"></a>
+
+## 5. Prediction
+
+Set parameters and use PaddleDetection to predict：
+
+```bash
+export CUDA_VISIBLE_DEVICES=0
+python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture
+```
+
+`--draw_threshold` is an optional parameter. According to the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659), different threshold will produce different results, ` keep_top_k ` represent  the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。
+
+<a name="Deployment"></a>
+
+## 6. Deployment
+
+Use your trained model in Layout Parser
+
+<a name="Export model"></a>
+
+### 6.1 Export model
+
+n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The `tools/export_model.py` script is provided in PaddleDetection to export the model.
+
+The exported model name defaults to `model.*`, Layout Parser's code model is `inference.*`, So change [PaddleDetection/ppdet/engine/trainer. Py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py# L512) (click on the link to see the detailed line of code), change 'model' to 'inference'.
+
+Execute the script to export model:
+
+```bash
+python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
+```
+
+The prediction model is exported to `inference/ppyolov2_r50vd_dcn_365e_coco` ,including:`infer_cfg.yml`(prediction not required), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel`
+
+More model export tutorials, please refer to：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
+
+<a name="Inference"></a>
+
+### 6.2 Inference
+
+`model_path` represent  the trained model path, and layoutparser is used to predict:
+
+```bash
+import layoutparser as lp
+model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)
+```
+
+
+
+***
+
+More PaddleDetection training tutorials，please reference：[PaddleDetection Training](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
+
+***

test1/layout/train_layoutparser_model.md → ppstructure/layout/train_layoutparser_model_ch.md

 # 训练版面分析
 
-* [1. 安装](#安装)
-  * [1.1 环境要求](#环境要求)
-  * [1.2 安装PaddleDetection](#安装PaddleDetection)
-* [2. 准备数据](#准备数据)
-* [3. 配置文件改动和说明](#配置文件改动和说明)
-* [4. PaddleDetection训练](#训练)
-* [5. PaddleDetection预测](#预测)
-* [6. 预测部署](#预测部署)
-  * [6.1 模型导出](#模型导出)
-  * [6.2 layout parser预测](#layout_parser预测)
+[1. 安装](#安装)
+
+​        [1.1 环境要求](#环境要求)
+
+​        [1.2 安装PaddleDetection](#安装PaddleDetection)
+
+[2. 准备数据](#准备数据)
+
+[3. 配置文件改动和说明](#配置文件改动和说明)
+
+[4. PaddleDetection训练](#训练)
+
+[5. PaddleDetection预测](#预测)
+
+[6. 预测部署](#预测部署)
+
+​        [6.1 模型导出](#模型导出)
+
+​        [6.2 layout parser预测](#layout_parser预测)
 
 <a name="安装"></a>
 
@@ -64,10 +73,10 @@ tar -xvf publaynet.tar.gz
 | `train/`       | Images in the training subset                    | 335,703 |
 | `val/`         | Images in the validation subset                  | 11,245  |
 | `test/`        | Images in the testing subset                     | 11,405  |
-| `train.json`   | Annotations for training images                  |         |
-| `val.json`     | Annotations for validation images                |         |
-| `LICENSE.txt`  | Plaintext version of the CDLA-Permissive license |         |
-| `README.txt`   | Text file with the file names and description    |         |
+| `train.json`   | Annotations for training images                  | 1       |
+| `val.json`     | Annotations for validation images                | 1       |
+| `LICENSE.txt`  | Plaintext version of the CDLA-Permissive license | 1       |
+| `README.txt`   | Text file with the file names and description    | 1       |
 
 如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
 
@@ -77,23 +86,30 @@ tar -xvf publaynet.tar.gz
 
 我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
 
-<div align='center'>
-  <img src='../../doc/table/PaddleDetection_config.png' width='600px'/>
-</div>
+```bash
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/ppyolov2_r50vd_dcn.yml',
+  './_base_/optimizer_365e.yml',
+  './_base_/ppyolov2_reader.yml',
+]
+
+snapshot_epoch: 8
+weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
+```
+从中可以看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
 
-从上图看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
+- coco_detection.yml：主要说明了训练数据和验证数据的路径
 
-```
-coco_detection.yml：主要说明了训练数据和验证数据的路径
+- runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
 
-runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
+- optimizer_365e.yml：主要说明了学习率和优化器的配置
 
-optimizer_365e.yml：主要说明了学习率和优化器的配置
+- ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
 
-ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
+- ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
 
-ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
-```
 
 根据实际情况，修改上述文件，比如数据集路径、batch size等。
 
@@ -147,7 +163,7 @@ python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer
 
 ## 6. 预测部署
 
-在layout parser中使用自己训练好的模型，
+在layout parser中使用自己训练好的模型。
 
 <a name="模型导出"></a>
 
@@ -185,4 +201,3 @@ model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_3
 更多PaddleDetection训练教程，请参考：[PaddleDetection训练](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
 
 ***
-