SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition

• URL: http://arxiv.org/abs/2506.21920v1
• Date: Fri, 27 Jun 2025 05:20:42 GMT
• Title: SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition
• Authors: Nam Quan Nguyen, Xuan Phong Pham, Tuan-Anh Tran,
• Abstract summary: We present SepFormer, which integrates the split-and-merge paradigm into a single step through separator regression with a DETR-style architecture.<n>SepFormer can run on average at 25.6 FPS while achieving comparable performance with state-of-the-art methods on several benchmark datasets.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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