SEMv3: A Fast and Robust Approach to Table Separation Line Detection

TL;DR

SEMv3 introduces a fast, robust method for detecting table separation lines using a novel Keypoint Offset Regression module, significantly improving accuracy and efficiency in table structure recognition tasks.

Contribution

The paper proposes SEMv3, a new approach combining split, embed, and merge stages with a KOR module for improved table line detection.

Findings

Achieves state-of-the-art performance on multiple datasets.

Detects table separation lines quickly and accurately.

Outperforms existing methods in robustness and speed.

Abstract

Table structure recognition (TSR) aims to parse the inherent structure of a table from its input image. The `"split-and-merge" paradigm is a pivotal approach to parse table structure, where the table separation line detection is crucial. However, challenges such as wireless and deformed tables make it demanding. In this paper, we adhere to the "split-and-merge" paradigm and propose SEMv3 (SEM: Split, Embed and Merge), a method that is both fast and robust for detecting table separation lines. During the split stage, we introduce a Keypoint Offset Regression (KOR) module, which effectively detects table separation lines by directly regressing the offset of each line relative to its keypoint proposals. Moreover, in the merge stage, we define a series of merge actions to efficiently describe the table structure based on table grids. Extensive ablation studies demonstrate that our proposed KOR module can detect table separation lines quickly and accurately. Furthermore, on public datasets (e.g. WTW, ICDAR-2019 cTDaR Historical and iFLYTAB), SEMv3 achieves state-of-the-art (SOTA) performance. The code is available at https://github.com/Chunchunwumu/SEMv3.