In view of the problem that it is difficult to accurately extract the table structure from the document,a two-branch reco-gnition network model integrating the graph convolutional network was proposed.ResNet+FPN was taken as the main stem network,and the matrix decomposition head was introduced instead of the attention mechanism to renormute the global features.A two-branch network was designed to obtain spatial location and logical adjacency information.Position information and logical adjacency were output by GCN sensing cell connection relationship.Experimental results show that compared with the baseline model,the F1 index is increased by 10.6％,and the F(beta=0.5)index is increased by 18.6％.In the TableGraph-24K data set,compared with the recent TGRNet model,the F1 index is improved by 3.1％,and the F(beta=0.5)index is improved by 2.9％on average.
维普
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