Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
This addresses the problem of accurately recognizing complex table structures for applications in document analysis, though it appears incremental as it builds on existing TSR methods with a novel component selection approach.
The paper tackles the Complex Table Structure Recognition (TSR) problem, where existing methods degrade due to inefficient component usage and redundant post-processing, by proposing GrabTab with a Component Deliberator that flexibly selects components, resulting in significant outperformance over state-of-the-art methods on public benchmarks, especially in challenging scenes.
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community. While impressive success, most single table component-based methods can not perform well on unregularized table cases distracted by not only complicated inner structure but also exterior capture distortion. In this paper, we raise it as Complex TSR problem, where the performance degeneration of existing methods is attributable to their inefficient component usage and redundant post-processing. To mitigate it, we shift our perspective from table component extraction towards the efficient multiple components leverage, which awaits further exploration in the field. Specifically, we propose a seminal method, termed GrabTab, equipped with newly proposed Component Deliberator. Thanks to its progressive deliberation mechanism, our GrabTab can flexibly accommodate to most complex tables with reasonable components selected but without complicated post-processing involved. Quantitative experimental results on public benchmarks demonstrate that our method significantly outperforms the state-of-the-arts, especially under more challenging scenes.