A GANBased Feature Generator for Table Detection Table detection is of great significance for the documents analysis and recognition. Although many methods have been proposed and great progress have been made, it is still a great challenge to recognize the lessruled tables due to the lack of table line features. In this paper, we propose a novel network to generate the layout features for table text to improve the performance of lessruled table recognition. This feature generator model is similar to the Generative Adversarial Networks (GAN). We force the feature generator model to extract similar features for both ruling tables and lessruled tables. It can be added into some common object detection and semantic segmentation models such as Mask RCNN, UNet. Extensive experiments are conducted on the dataset of ICDAR2017 Page Object Detection Competition dataset and a closed dataset full of the lessruled tables and nonruled tables. The primary experimental results show that the proposed GANbased feature generator is very helpful for lessruled table detection.

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.