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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 (combined with Layout-Parser)
- Support to extract the texts from the text, title, picture and list areas (combined with PP-OCR)
- Support to extract excel files from the table areas
- Support to use with python whl package and command line easily
- Support custom training for layout analysis and table structure tasks
- （1) Install PaddlePaddle
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 .
- (2) Install Layout-Parser
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl- （1) PIP install PaddleOCR whl package（inference only）
pip install "paddleocr>=2.2"- （2) Clone PaddleOCR（Inference+training）
git clone https://github.com/PaddlePaddle/PaddleOCRpaddleocr --image_dir=../doc/table/1.png --type=structureimport 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')The returned results of PP-Structure is a list composed of a dict, an example is as follows
[
{ '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。 Table: HTML string of the table; OCR: A tuple containing the detection coordinates and recognition results of each single line of text |
| 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
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.
the process is as follows
In PP-Structure, 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.
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.
Table Recognition 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
Use the following commands to complete the inference.
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 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 --output=../output/table --vis_font_path=../doc/fonts/simfang.ttfAfter 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 | 18.6M | inference model |
Model List
LayoutParser model
| model name | description | download |
|---|---|---|
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet data set can be divided into 5 types of areas text, title, table, picture and list | PubLayNet |
| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset can only detect tables | TableBank Word |
| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset can only detect tables | TableBank Latex |
OCR and table recognition model
| model name | description | model size | download |
|---|---|---|---|
| ch_ppocr_mobile_slim_v2.0_det | Slim pruned lightweight model, supporting Chinese, English, multilingual text detection | 2.6M | inference model / trained model |
| ch_ppocr_mobile_slim_v2.0_rec | Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition | 6M | inference model / trained model |
| en_ppocr_mobile_v2.0_table_det | Text detection of English table scenes trained on PubLayNet dataset | 4.7M | inference model / trained model |
| en_ppocr_mobile_v2.0_table_rec | Text recognition of English table scene trained on PubLayNet dataset | 6.9M | inference model trained model |
| en_ppocr_mobile_v2.0_table_structure | Table structure prediction of English table scene trained on PubLayNet dataset | 18.6M | inference model / trained model |
If you need to use other models, you can download the model in model_list or use your own trained model to configure it to the three fields of det_model_dir, rec_model_dir, table_model_dir .