Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling

TL;DR

This paper introduces VAST, an end-to-end framework that improves table structure recognition by modeling bounding box coordinates as sequences and incorporating visual alignment, leading to state-of-the-art results.

Contribution

The paper proposes a novel coordinate sequence decoder and visual-alignment loss to enhance logical and physical table structure recognition.

Findings

Achieves state-of-the-art performance in table structure recognition.

The coordinate sequence decoder improves bounding box accuracy.

Visual-alignment loss enhances local visual detail in logical representations.

Abstract

Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, the previous methods struggle with imprecise bounding boxes as the logical representation lacks local visual information. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.