English | 简体中文

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 (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. Visualization

2. Installation

2.1 Install requirements

• （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

2.2 Install PaddleOCR（including PP-OCR and PP-Structure）

• （1) PIP install PaddleOCR whl package（inference only）

pip install "paddleocr>=2.2"

• （2) Clone PaddleOCR（Inference+training）

git clone https://github.com/PaddlePaddle/PaddleOCR

3. Quick Start

3.1 Use by command line

paddleocr --image_dir=../doc/table/1.png --type=structure

3.2 Use by python API

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 Returned results format

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

3.4 Parameter description：

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.

4. PP-Structure Pipeline

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.

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.

4.2 Table Recognition

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

5. Prediction by inference engine

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.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 List

LayoutParser model

OCR and table recognition 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 .