77 lines
2.2 KiB
Markdown
77 lines
2.2 KiB
Markdown
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# 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. PaddleStructure whl package introduction
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### 4.1 Use
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4.1.1 Use by code
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```python
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import cv2
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from paddlestructure import PaddleStructure,draw_result
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table_engine = PaddleStructure(
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output='./output/table',
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show_log=True)
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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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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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4.1.2 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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### 参数说明
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大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
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| 字段 | 说明 | 默认值 |
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|------------------------|------------------------------------------------------|------------------|
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| output | excel和识别结果保存的地址 | ./output/table |
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| structure_max_len | structure模型预测时，图像的长边resize尺度 | 488 |
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| structure_model_dir | structure inference 模型地址 | None |
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| structure_char_type | structure 模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.tx |
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