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PP-Structure Quick Start

1. Install package

# Install paddleocr, version 2.5+ is recommended
pip3 install "paddleocr>=2.5"
# Install layoutparser (if you do not use the layout analysis, you can skip it)
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
# Install the DocVQA dependency package paddlenlp (if you do not use the DocVQA, you can skip it)
pip install paddlenlp

2. Use

2.1 Use by command line

2.1.1 layout analysis + table recognition

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure

2.1.2 layout analysis

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure --table=false --ocr=false

2.1.3 table recognition

paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/table.jpg --type=structure --layout=false

2.1.4 DocVQA

Please refer to: Documentation Visual Q&A .

2.2 Use by code

2.2.1 layout analysis + table recognition

import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res

table_engine = PPStructure(show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/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 = 'PaddleOCR/doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
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')

2.2.2 layout analysis

import os
import cv2
from paddleocr import PPStructure,save_structure_res

table_engine = PPStructure(table=False, ocr=False, show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/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)

2.2.3 table recognition

import os
import cv2
from paddleocr import PPStructure,save_structure_res

table_engine = PPStructure(layout=False, show_log=True)

save_folder = './output'
img_path = 'PaddleOCR/ppstructure/docs/table/table.jpg'
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)

2.2.4 DocVQA

Please refer to: Documentation Visual Q&A .

2.3 Result description

The return of PP-Structure is a list of dicts, the example is as follows:

2.3.1 layout analysis + table recognition

[
  {   '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)])
  }
]

Each field in dict is described as follows:

field description
type Type of image area.
bbox The coordinates of the image area in the original image, respectively [upper left corner x, upper left corner y, lower right corner x, lower right corner y].
res OCR or table recognition result of the image area.
table: a dict with field descriptions as follows:
        html: html str of table.
        In the code usage mode, set return_ocr_result_in_table=True whrn call can get the detection and recognition results of each text in the table area, corresponding to the following fields:
        boxes: text detection boxes.
        rec_res: text recognition results.
OCR: A tuple containing the detection boxes and recognition results of each single text.

After the recognition is completed, each image will have a directory with the same name under the directory specified by the output field. Each table in the image will be stored as an excel, and the picture area will be cropped and saved. The filename of excel and picture is their coordinates in the image.

/output/table/1/
  └─ res.txt
  └─ [454, 360, 824, 658].xlsx        table recognition result
  └─ [16, 2, 828, 305].jpg            picture in Image
  └─ [17, 361, 404, 711].xlsx        table recognition result

2.3.2 DocVQA

Please refer to: Documentation Visual Q&A .

2.4 Parameter Description

field description default
output The save path of result ./output/table
table_max_len When the table structure model predicts, the long side of the image 488
table_model_dir the path of table structure model None
table_char_dict_path the dict path of table structure model ../ppocr/utils/dict/table_structure_dict.txt
layout_path_model The model path of the layout analysis model, which can be an online address or a local path. When it is a local path, layout_label_map needs to be set. In command line mode, use --layout_label_map='{0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}' lp:https://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config
layout_label_map Layout analysis model model label mapping dictionary path None
model_name_or_path the model path of VQA SER model None
max_seq_length the max token length of VQA SER model 512
label_map_path the label path of VQA SER model ./vqa/labels/labels_ser.txt
layout Whether to perform layout analysis in forward True
table Whether to perform table recognition in forward True
ocr Whether to perform ocr for non-table areas in layout analysis. When layout is False, it will be automatically set to False True

Most of the parameters are consistent with the PaddleOCR whl package, see whl package documentation