Table Structure Recognition

Table structure recognition studies how to recover a table’s rows, columns, cell boundaries, spanning cells, and hierarchy from images or document pages, often alongside cell text. Approaches include grid and object detection, graph-based prediction, autoregressive HTML or OTSL generation, and vision-language models guided by spatial or structural priors. Systems are commonly evaluated on benchmarks such as PubTabNet, FinTabNet, and PubTables-1M using structural and content metrics such as TEDS and GriTS. A central challenge is handling complex, degraded, or unfamiliar tables: strong scores on clean benchmarks do not guarantee reliable real-world extraction, and accurate structure alone does not ensure sound question answering or arithmetic.