  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.
