Order-Independent Cell-Level Representations for Multi-Task Table Recognition

This paper introduces a structural refinement module using non-causal attention to generate order-independent cell features in autoregressive multi-task table recognition. The approach enables parallel cell content inference while maintaining global context, improving cell localization and end-to-end recognition with a threefold reduction in inference time.