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Recently, Table Structure Recognition (TSR) task, aiming at identifying tablestructure into machine readable formats, has received increasing interest inthe community. While impressive success, most single table component-basedmethods can not perform well on unregularized table cases distracted by notonly complicated inner structure but also exterior capture distortion. In thispaper, we raise it as Complex TSR problem, where the performance degenerationof existing methods is attributable to their inefficient component usage andredundant post-processing. To mitigate it, we shift our perspective from tablecomponent extraction towards the efficient multiple components leverage, whichawaits further exploration in the field. Specifically, we propose a seminalmethod, termed GrabTab, equipped with newly proposed Component Deliberator.Thanks to its progressive deliberation mechanism, our GrabTab can flexiblyaccommodate to most complex tables with reasonable components selected butwithout complicated post-processing involved. Quantitative experimental resultson public benchmarks demonstrate that our method significantly outperforms thestate-of-the-arts, especially under more challenging scenes.

Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation