Processing tables as structured text instead of images makes them easier for LLMs to understand, scales better across languages, and achieves better performance on table QA tasks without needing visual encoders.
This paper presents DELTA and TARQA, a two-stage approach for table understanding that converts table images into structured text (OTSL format) rather than relying on vision-language models. DELTA handles table structure recognition and OCR, while TARQA is an LLM fine-tuned to answer questions about tables in this text format.