InstructTable: Improving Table Structure Recognition Through Instructions

• URL: http://arxiv.org/abs/2604.02880v1
• Date: Fri, 03 Apr 2026 08:44:45 GMT
• Title: InstructTable: Improving Table Structure Recognition Through Instructions
• Abstract summary: Table structure recognition (TSR) holds widespread practical importance.<n>Traditional visual-centric models rely exclusively on visual information while lacking crucial semantic support.<n> Vision-guided table instruction pre-training addresses these limitations.
• Score: 18.36576098544595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Table structure recognition (TSR) holds widespread practical importance by parsing tabular images into structured representations, yet encounters significant challenges when processing complex layouts involving merged or empty cells. Traditional visual-centric models rely exclusively on visual information while lacking crucial semantic support, thereby impeding accurate structural recognition in complex scenarios. Vision-language models leverage contextual semantics to enhance comprehension; however, these approaches underemphasize the modeling of visual structural information. To address these limitations, this paper introduces InstructTable, an instruction-guided multi-stage training TSR framework. Meticulously designed table instruction pre-training directs attention toward fine-grained structural patterns, enhancing comprehension of complex tables. Complementary TSR fine-tuning preserves robust visual information modeling, maintaining high-precision table parsing across diverse scenarios. Furthermore, we introduce Table Mix Expand (TME), an innovative template-free method for synthesizing large-scale authentic tabular data. Leveraging TME, we construct the Balanced Complex Dense Synthetic Tables (BCDSTab) benchmark, comprising 900 complex table images synthesized through our method to serve as a rigorous benchmark. Extensive experiments on multiple public datasets (FinTabNet, PubTabNet, MUSTARD) and BCDSTab demonstrate that InstructTable achieves state-of-the-art performance in TSR tasks. Ablation studies further confirm the positive impact of the proposed tabular-data-specific instructions and synthetic data.

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