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Recently, table structure recognition has achieved impressive progress withthe help of deep graph models. Most of them exploit single visual cues oftabular elements or simply combine visual cues with other modalities via earlyfusion to reason their graph relationships. However, neither early fusion norindividually reasoning in terms of multiple modalities can be appropriate forall varieties of table structures with great diversity. Instead, differentmodalities are expected to collaborate with each other in different patternsfor different table cases. In the community, the importance of intra-intermodality interactions for table structure reasoning is still unexplored. Inthis paper, we define it as heterogeneous table structure recognition(Hetero-TSR) problem. With the aim of filling this gap, we present a novelNeural Collaborative Graph Machines (NCGM) equipped with stacked collaborativeblocks, which alternatively extracts intra-modality context and modelsinter-modality interactions in a hierarchical way. It can represent theintra-inter modality relationships of tabular elements more robustly, whichsignificantly improves the recognition performance. We also show that theproposed NCGM can modulate collaborative pattern of different modalitiesconditioned on the context of intra-modality cues, which is vital fordiversified table cases. Experimental results on benchmarks demonstrate ourproposed NCGM achieves state-of-the-art performance and beats othercontemporary methods by a large margin especially under challenging scenarios.

Neural Collaborative Graph Machines for Table Structure Recognition