AI RESEARCH
DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates
arXiv CS.CV
•
ArXi:2604.16099v1 Announce Type: new Tables condense key transactional and administrative information into compact layouts, but practical extraction requires than text recognition: systems must also recover structure (rows, columns, merged cells, headers) and interpret roles such as line items, subtotals, and totals under common capture artifacts. Many existing resources for table structure recognition and TableVQA are built from clean digital-born sources or rendered tables, and. therefore. only partially reflect noisy administrative conditions. To improve arithmetic reliability without.