Tables Decoded: DELTA for Structure, TARQA for Understanding

Table 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. These representations are easier to process, align more naturally with large language models (LLMs), and eliminate the need for language-specific visual encoders, making them particularly suitable for multilingual documents. We present DELTA, a decoupled table reconstruction framework that separates structure recognition from OCR to extract both layout and content accurately. DELTA outputs tables in Optimized Table Structure Language (OTSL), a compact and unified format that encodes cell arrangements and textual content. DELTA achieves high-fidelity table-to-text conversion, outperforming prior methods on structure metrics with superior TEDS-Structure scores across FinTabNet, PubTabNet, and PubTables. On FinTabNet, it surpasses the best VLM baseline by an absolute 0.4% in overall TEDS score. Built on DELTA, we introduce TARQA (Table structure-Aware Representation for Question Answering), an LLM fine-tuned on OTSL-formatted tables for accurate and structure-aware TabVQA. TARQA outperforms baselines fine-tuned on HTML representations by 14.2%, and improves answer accuracy on WTQ by 6.8% and on FinTabNetQA by 9.2%. We release our code and models to advance research in multilingual, structure-aware table understanding.