Tables Decoded: DELTA for Structure, TARQA for Understanding
DELTA extracts tables into compact OTSL text and TARQA fine-tunes LLMs on it, beating VLM baselines on table QA.
DELTA separates physical structure recognition, logical structure recognition, and OCR to output tables in Optimised Table Structure Language (OTSL), a compact unified format encoding cell arrangements and content. It achieves TEDS-Structure scores comparable to state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M, with robustness tested on a curated Hindi benchmark, TORQUE. TARQA, an LLM fine-tuned on OTSL sequences, gains 9.3 percentage points on WTQ TabQA and 9.2 points on FinTabNetQA TabVQA; code, models, and the benchmark are released on GitHub.
- Structured text representation replaces VLM table-image pipelines
- DELTA matches SOTA TEDS-Structure on FinTabNet, PubTabNet, PubTables-1M
- TARQA gains 9.3 p.p. on WTQ and 9.2 p.p. on FinTabNetQA
- New Hindi benchmark TORQUE tests multilingual table robustness
From the original article
1 min read at arxiv.orgTable understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rely on vision- language models (VLMs) operating on table images, we propose a more scalable and effective alternative based on structured textual representations.
By Jahanvi Rajput · arxiv.org
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