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 perception of complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically rely on direct supervision of the final output, thereby bypassing the explicit intermediate reasoning that is crucial for understanding complex table structures. Furthermore, attempts to optimize these models using reinforcement learning (RL) are often hindered by unstable or ambiguous reward designs, limiting potential performance gains. To address these limitations, we propose StrucTab, a table parsing model learned through in...
| Authors | Gengluo Li, Shangpin Peng, Chengquan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 June 2026 |
| Last updated at source | 27 September 2026 |
Models and datasets referencing this paper
- psp-dada/TableVerse-5KDataset
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