# Rethinking Table Recognition using Graph Neural Networks

arXiv: https://arxiv.org/abs/1905.13391 · HTML: https://mlsanity.com/paper/1905.13391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 5 of 5)

- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables](https://mlsanity.com/paper/2105.06400/) (2021)
- [Multi-Modal Association based Grouping for Form Structure Extraction](https://mlsanity.com/paper/2107.04396/) (2021)
- [TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words](https://mlsanity.com/paper/2203.06873/) (2022)
- [VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction](https://mlsanity.com/paper/2407.06826/) (2024)

## Abstract (arXiv)

Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since conventional neural networks are not well suited to the input structure of the problem. In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition. We argue that graph networks are a more natural choice for these problems, and explore two gradient-based graph neural networks. Our proposed architecture combines the benefits of convolutional neural networks for visual feature extraction and graph networks for dealing with the problem structure. We empirically demonstrate that our method outperforms the baseline by a significant margin. In addition, we identify the lack of large scale datasets as a major hindrance for deep learning research for structure analysis and present a new large scale synthetic dataset for the problem of table recognition. Finally, we open-source our implementation of dataset generation and the training framework of our graph networks to promote reproducible research in this direction.