  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.
