87 lines
3.1 KiB
Markdown
87 lines
3.1 KiB
Markdown
# PaddleStructure
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install layoutparser
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```sh
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wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
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pip3 install layoutparser-0.0.0-py3-none-any.whl
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```
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## 1. Introduction to pipeline
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PaddleStructure is a toolkit for complex layout text OCR, the process is as follows
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In PaddleStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, and the OCR process will be carried out according to the category.
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Currently layoutparser will output five categories:
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1. Text
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2. Title
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3. Figure
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4. List
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5. Table
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Types 1-4 follow the traditional OCR process, and 5 follow the Table OCR process.
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## 2. LayoutParser
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## 3. Table OCR
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[doc](table/README.md)
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## 4. Predictive by inference engine
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Use the following commands to complete the inference
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```python
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python3 table/predict_system.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
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```
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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 the excel file name will be the coordinates of the table in the image.
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## 5. PaddleStructure whl package introduction
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### 5.1 Use
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5.1.1 Use by code
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```python
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import os
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import cv2
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from paddlestructure import PaddleStructure,draw_result,save_res
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table_engine = PaddleStructure(show_log=True)
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save_folder = './output/table'
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img_path = '../doc/table/1.png'
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img = cv2.imread(img_path)
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result = table_engine(img)
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save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
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for line in result:
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print(line)
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from PIL import Image
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font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
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image = Image.open(img_path).convert('RGB')
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im_show = draw_result(image, result,font_path=font_path)
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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5.1.2 Use by command line
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```bash
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paddlestructure --image_dir=../doc/table/1.png
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```
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### Parameter Description
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Most of the parameters are consistent with the paddleocr whl package, see [whl package documentation](../doc/doc_ch/whl.md)
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| Parameter | Description | Default |
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|------------------------|------------------------------------------------------|------------------|
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| output | The path where excel and recognition results are saved | ./output/table |
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| structure_max_len | When the table structure model predicts, the long side of the image is resized | 488 |
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| structure_model_dir | Table structure inference model path | None |
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| structure_char_type | Dictionary path used by table structure model | ../ppocr/utils/dict/table_structure_dict.tx |
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