Transformer Based Spatial Reasoning For Table Structure

via “transformer-based-spatial-reasoning-for-table-structure”

object-detection model by undefined. 12,70,637 downloads.

Unique: Leverages multi-head self-attention in the transformer decoder to model long-range spatial dependencies between table elements, allowing the model to reason about alignment and grouping without explicit geometric constraints. This learned spatial reasoning is more flexible than rule-based alignment detection and generalizes better to diverse table styles.

vs others: More robust than CNN-only detectors on borderless or irregular tables because attention mechanisms capture semantic relationships; more flexible than geometric constraint-based methods (which assume regular grids) because it learns spatial patterns from data; more accurate than heuristic alignment detection on diverse document types