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

doc/doc_ch/whl.md

@@ -401,7 +401,6 @@ im_show.save('result.jpg')
 | rec_algorithm           | 使用的识别算法类型                                                                                                                                                                                                   | CRNN                    |
 | rec_model_dir          | 识别模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
 | rec_image_shape         | 识别算法的输入图片尺寸                                                                                                                                                                                             | "3,32,320"              |
-| rec_char_type           | 识别算法的字符类型，中英文(ch)、英文(en)、法语(french)、德语(german)、韩语(korean)、日语(japan)                                                                                                                                                                               | ch                      |
 | rec_batch_num           | 进行识别时，同时前向的图片数                                                                                                                                                                                         | 30                      |
 | max_text_length         | 识别算法能识别的最大文字长度                                                                                                                                                                                         | 25                      |
 | rec_char_dict_path      | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径                                                                                                                                                | ./ppocr/utils/ppocr_keys_v1.txt                        |

doc/doc_en/inference_en.md

@@ -296,7 +296,7 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
 
 - The image resolution used in training is different: the image resolution used in training the above model is [3，32，100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`.
 
-- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. Therefore, the parameter `rec_char_type` needs to be set during inference, which is specified as "en" in English.
+- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command.
 
 ```
 self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
@@ -320,7 +320,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 
 <a name="USING_CUSTOM_CHARACTERS"></a>
 ### 3.4 Text Recognition Model Inference Using Custom Characters Dictionary
-If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
+If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`
 
 ```
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100"  --rec_char_dict_path="your text dict path"

doc/doc_en/inference_ppocr_en.md

@@ -4,12 +4,13 @@
 This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
 
 
-- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
-- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
-    - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
-    - [2. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
-- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
-- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
+- [Python Inference for PP-OCR Model Zoo](#python-inference-for-pp-ocr-model-zoo)
+  - [Text Detection Model Inference](#text-detection-model-inference)
+  - [Text Recognition Model Inference](#text-recognition-model-inference)
+    - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference)
+    - [2. Multilingual Model Inference](#2-multilingual-model-inference)
+  - [Angle Classification Model Inference](#angle-classification-model-inference)
+  - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation)
 
 <a name="DETECTION_MODEL_INFERENCE"></a>
 
@@ -82,7 +83,7 @@ You need to specify the visual font path through `--vis_font_path`. There are sm
 ```
 wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
 
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
 

doc/doc_en/quickstart_en.md

+- [PaddleOCR Quick Start](#paddleocr-quick-start)
+  - [1. Installation](#1-installation)
+    - [1.1 Install PaddlePaddle](#11-install-paddlepaddle)
+    - [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package)
+  - [2. Easy-to-Use](#2-easy-to-use)
+    - [2.1 Use by Command Line](#21-use-by-command-line)
+      - [2.1.1 Chinese and English Model](#211-chinese-and-english-model)
+      - [2.1.2 Multi-language Model](#212-multi-language-model)
+      - [2.1.3 Layout Analysis](#213-layout-analysis)
+    - [2.2 Use by Code](#22-use-by-code)
+      - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese--english-model-and-multilingual-model)
+      - [2.2.2 Layout Analysis](#222-layout-analysis)
+  - [3. Summary](#3-summary)
 
 # PaddleOCR Quick Start
 
-+ [1. Installation](#1installation)
-  + [1.1 Install PaddlePaddle](#11-install-paddlepaddle)
-  + [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package)
-* [2. Easy-to-Use](#2-easy-to-use)
-  + [2.1 Use by Command Line](#21-use-by-command-line)
-    - [2.1.1 English and Chinese Model](#211-english-and-chinese-model)
-    - [2.1.2 Multi-language Model](#212-multi-language-model)
-    - [2.1.3 Layout Analysis](#213-layoutAnalysis)
-  + [2.2 Use by Code](#22-use-by-code)
-    - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese---english-model-and-multilingual-model)
-    - [2.2.2 Layout Analysis](#222-layoutAnalysis)
-* [3. Summary](#3)
 
 <a name="1nstallation"></a>
 
@@ -196,7 +197,7 @@ paddleocr --image_dir=../doc/table/1.png --type=structure
   | 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.txt |
+  | table_char_dict_path | dict path of table structure model                           | ../ppocr/utils/dict/table_structure_dict.txt |
 
 <a name="22-use-by-code"></a>
 

doc/doc_en/recognition_en.md

@@ -470,8 +470,8 @@ inference/det_db/
 
 - Text recognition model Inference using custom characters dictionary
 
-  If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
+  If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`
 
   ```
-  python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
+  python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
   ```

doc/doc_en/whl_en.md

@@ -348,7 +348,6 @@ im_show.save('result.jpg')
 | rec_algorithm           | Type of recognition algorithm selected                                                                                                                                                                                                | CRNN                    |
 | rec_model_dir           | the text recognition inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/rec`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
 | rec_image_shape         | image shape of recognition algorithm                                                                                                                                                                                            | "3,32,320"              |
-| rec_char_type           | Character type of recognition algorithm, Chinese (ch) or English (en)                                                                                                                                                                               | ch                      |
 | rec_batch_num           | When performing recognition, the batchsize of forward images                                                                                                                                                                                         | 30                      |
 | max_text_length         | The maximum text length that the recognition algorithm can recognize                                                                                                                                                                                         | 25                      |
 | rec_char_dict_path      | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2                                                                                                                                              | ./ppocr/utils/ppocr_keys_v1.txt                        |

ppstructure/table/README.md

@@ -117,7 +117,7 @@ teds: 93.32
 
 ```python
 cd PaddleOCR/ppstructure
-python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
+python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
 ```
 After running, the excel sheet of each picture will be saved in the directory specified by the output field
 

ppstructure/table/README_ch.md

@@ -117,7 +117,7 @@ teds: 93.32
 
 ```python
 cd PaddleOCR/ppstructure
-python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
+python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
 ```
 
 Reference

ppstructure/table/predict_structure.py

@@ -58,7 +58,6 @@ class TableStructurer(object):
         }]
         postprocess_params = {
             'name': 'TableLabelDecode',
-            "character_type": args.table_char_type,
             "character_dict_path": args.table_char_dict_path,
         }
 
@@ -104,7 +103,9 @@ class TableStructurer(object):
             res_loc_final.append([left, top, right, bottom])
 
         structure_str_list = structure_str_list[0][:-1]
-        structure_str_list = ['<html>', '<body>', '<table>'] + structure_str_list + ['</table>', '</body>', '</html>']
+        structure_str_list = [
+            '<html>', '<body>', '<table>'
+        ] + structure_str_list + ['</table>', '</body>', '</html>']
 
         elapse = time.time() - starttime
         return (structure_str_list, res_loc_final), elapse

ppstructure/utility.py

@@ -26,7 +26,6 @@ def init_args():
     # params for table structure
     parser.add_argument("--table_max_len", type=int, default=488)
     parser.add_argument("--table_model_dir", type=str)
-    parser.add_argument("--table_char_type", type=str, default='en')
     parser.add_argument(
         "--table_char_dict_path",
         type=str,