bygreencn / CascadeTabNet

This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"

CascadeTabNet

License

The code of CascadeTabNet is released under the MIT License. There is no limitation for both acadmic and commercial usage.

Paper

Preprint Link of Paper : The paper has been accepted at CVPR 2020 Workshop

End to End Table Recognition Dataset

We manually annotated some of the ICDAR 19 table competition (cTDaR) dataset images. Details about the dataset are mentioned in the paper. dataset link

Introduction

CascadTabNet is an automatic table recognition method for interpretation of tabular data in document images. We present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. CascadeTabNet is a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and TableBank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset.

We use MMdetection framework to implement the model.

Model Architecture

Model Computation Graph

Image Augmentation

Codes: Code for dilation transform Code for smudge transform

Benchmarking

Table Detection

1. ICDAR 13

2. ICDAR 19 (Track A Modern)

3. TableBank

TableBank Benchmarking : Leaderboard
TableBank Dataset Divisions : TableBank

Table Structure Recognition

1. ICDAR 19 (Track B2)

Model Zoo

Checkpoints of the Models we have trained :

Model Name	Checkpoint File
General Model table detection	Checkpoint
ICDAR 13 table detection	Checkpoint
ICDAR 19 (Track A Modern) table detection	Checkpoint
Table Bank Word table detection	Checkpoint
Table Bank Latex table detection	Checkpoint
Table Bank Both table detection	Checkpoint
ICDAR 19 (Track B2 Modern) table structure recognition	Checkpoint

Additional Results

Supplementary file

The whole code will be released soon in this repository !

Contact

Devashish Prasad : [e-mail]
Ayan Gadpal : [e-mail]
Kshitij Kapadni : [e-mail]
Manish Visave : [e-mail]

Acknowledgement

We thank Akshay Navalakha (AP Analytica) for his idea and guidance in the initial project of invoice-document parsing that we developed for him.

Cite as

@misc{ cascadetabnet2020,
    title={CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents},
    author={Devashish Prasad and Ayan Gadpal and Kshitij Kapadni and Manish Visave and Kavita Sultanpure},
    year={2020},
    eprint={2004.12629},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}