抓住你所需要的：用灵活组件重新思考复杂表格结构识别
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
摘要：
最近，表结构识别（TSR）任务，旨在识别表结构到机器可读的格式，在社会上受到越来越多的关注。虽然基于单表组件的方法取得了令人印象深刻的成功，但大多数基于单表组件的方法不能很好地处理因内部结构复杂和外部捕获失真而分散注意力的非正则表情况。在本文中，我们将其提出为复杂的TSR问题，其中现有方法的性能退化归因于其低效的组件使用和冗余的后处理。为了减轻它，我们将我们的视角从表组件提取转向有效的多组件利用，这有待于在该领域的进一步探索。具体来说，我们提出了一个开创性的方法，称为GrabTab，配备了新提出的组件审议。由于其渐进式的审议机制，我们的GrabTab可以灵活地适应最复杂的表格，选择合理的组件，但不涉及复杂的后处理。公共基准上的定量实验结果表明，我们的方法显着优于最先进的，特别是在更具挑战性的场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
