Multi-Type-TD-TSR

Check it out on Source Code of our Paper: Multi-Type-TD-TSR Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition

Description

Multi-Type-TD-TSR the Whole Pipeline

Multi-Type-TD-TSR on Fully Bordered Tables

Multi-Type-TD-TSR on Unbordered Tables

Multi-Type-TD-TSR on Partially Bordered Tables

Table Structure Recognition Results

ICDAR 19 (Track B2)

Instructions

Configurations

The source code is developed under the following library dependencies

• PyTorch = 1.7.0
• Torchvision = 0.8.1
• Cuda = 10.1
• PyYAML = 5.1

Detectron 2

The table detection model is based on detectron2 follow this installation guide to setup.

Image Alignment Pre-Processing

For the image alignment pre-processing step there is one script available:

• deskew.py

To apply the image alignment pre-processing algorithm to all images in one folder, you need to execute:

python3 deskew.py

with the following parameters

• --folder the input folder including document images
• --output the output folder for the deskewed images

Table Structure Recognition (TSR)

For the table structure recognition we offer a simple script for different approaches

• tsr.py

To apply a table structure recognition algorithm to all images in one folder, you need to execute:

python3 tsr.py

with the following parameters

• --folder path of the input folder including table images
• --type the table structure recognition type type in ["borderd", "unbordered", "partially", "partially_color_inv"]
• --img_output output folder path for the processed images
• --xml_output output folder path for the xml files including bounding boxes

Table Detection and Table Structure Recognition (TD & TSR)

To appy the table detection with a followed table structure recogniton

• tdtsr.py

To apply a table structure recognitio algorithm to all images in one folder, you need to execute:

python3 tdtsr.py

with the following parameters

• --folder path of the input folder including table images
• --type the table structure recognition type type in ["borderd", "unbordered", "partially", "partially_color_inv"]
• --tsr_img_output output folder path for the processed table images
• --td_img_output output folder path for the produced table cutouts
• --xml_output output folder path for the xml files for tables and cells including bounding boxes
• --config path of detectron2 configuration file for table detection
• --yaml path of detectron2 yaml file for table detection
• --weights path of detectron2 model weights for table detection

Evaluation

To evaluate the table structure recognition algorithm we provide the following script:

• evaluate.py

to apply the evaluation the table images and their labels in xml-format have to be the same name and should lie in a single folder. The evaluation could be started by:

python3 evaluate.py

with the following parameter

• --dataset dataset folder path containing table images and labels in .xml format

Get Data

• test dataset for table structure recognition including table images and annotations can be downloaded here
• table detection detectron2 model weights and configuration files can be downloaded here

Citation

@misc{fischer2021multitypetdtsr,
    title={Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations},
    author={Pascal Fischer and Alen Smajic and Alexander Mehler and Giuseppe Abrami},
    year={2021},
    eprint={2105.11021},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}