SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition (2506.21920v1)

Abstract: The automated reconstruction of the logical arrangement of tables from image data, termed Table Structure Recognition (TSR), is fundamental for semantic data extraction. Recently, researchers have explored a wide range of techniques to tackle this problem, demonstrating significant progress. Each table is a set of vertical and horizontal separators. Following this realization, we present SepFormer, which integrates the split-and-merge paradigm into a single step through separator regression with a DETR-style architecture, improving speed and robustness. SepFormer is a coarse-to-fine approach that predicts table separators from single-line to line-strip separators with a stack of two transformer decoders. In the coarse-grained stage, the model learns to gradually refine single-line segments through decoder layers with additional angle loss. At the end of the fine-grained stage, the model predicts line-strip separators by refining sampled points from each single-line segment. Our SepFormer can run on average at 25.6 FPS while achieving comparable performance with state-of-the-art methods on several benchmark datasets, including SciTSR, PubTabNet, WTW, and iFLYTAB.