Research on Table Structure Recognition Network Based on Row and Column Information Gates
To address the issue of high fault tolerance in detecting table rows and columns,this paper proposes two information transmission modules:row information gate and column information gate.By combining feature slicing and tiling,the row or column information gates are predicted to ensure the accuracy of simplified row and column prediction results.By utilizing both row and column information gates,the construction of a semantic segmentation model is completed to achieve precise segmentation of table rows and columns.Additionally,based on the ICDAR format dataset,the effective construction of table row and column masks is completed,and the performance of the model is evaluated.The results show that compared with the segmentation model based on the feature pyramid network,the semantic segmentation network model proposed in this paper has higher average precision,recall,and F1 value.Specifically,the average precision exceeds 0.55%,the recall rate exceeds 2.78%,and the F1 value exceeds 1.48%.It is hoped that this research can provide effective reference and inspiration for related personnel.
row and column information gatestable structure recognitionnetwork modelICDAR format
NETL
NSTL
万方数据
