Versatile Layout Understanding via Conjugate Graph Recent advances in document understanding, especially text recognition, provide new opportunities to address the page segmentation problem. In this paper, we propose a method to groups text lines into semantic objects. We model a page as a graph where nodes represent text lines and the edges their geometric relations. The logical segmentation task then refers to identify all text lines belonging to some logical subdivision of the page. We model this task as categorizing edges as relevant or not to build the targeted subdivision (subgraph). This edge categorization is performed using structured machine learning algorithms (graph Conditional Random Field and Edge Convolutional Network). We use a connected componentsbased approach following the edge classification for aggregating the nodes. This simple approach shows very robust results for various layout and various page subdivision. We experiment on table segmentation into multiple subdivisions (rows, columns, and cells) and minutes segmentation into resolutions. Our subdivision and pagelayout oblivious approach shows nearpar performance as compared to task dedicated approaches and even outperforms them in certain setups.

Rethinking Table Recognition using Graph Neural Networks 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 gradientbased 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 opensource our implementation of dataset generation and the training framework of our graph networks to promote reproducible research in this direction.