StrucTab: A Structured Optimization Framework for Table Parsing

Table parsing aims to convert table images into structured,machine-readable representations, a task requiring the joint perceptionof complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically relyon direct supervision of the final output, thereby bypassing the explicitintermediate reasoning that is crucial for understanding complex tablestructures. Furthermore, attempts to optimize these models using re-inforcement learning (RL) are often hindered by unstable or ambigu-ous reward designs, limiting potential performance gains. To addressthese limitations, we propose StrucTab, a table parsing model learnedthrough intermediate structural supervision and reward decomposition.At the modeling level, by decomposing the parsing process into human-inspired subtasks, such as row-column counting and merged-cell analy-sis, StrucTab progressively unifies them through a sequential reasoningstrategy. At the optimization level, we introduce Uni-TabRL, a unifiedRL framework that leverages decomposed rewards (validity, structure,and content) to provide stable and informative optimization signals. Fi-nally, at the evaluation level, we present TableVerse-5K, a large-scale,challenging benchmark encompassing diverse, real-world table scenarios.Extensive experiments demonstrate the state-of-the-art performance ofStrucTab across all evaluated public benchmarks and significant improve-ments on TableVerse-5K, validating the effectiveness of explicit structuralmodeling and decomposed reward optimization. Code and benchmark arepublicly available at https://github.com/VirtualLUOUCAS/StrucTab.