LGDNet:Table Detection Network Combining Local and Global Features
In the era of big data,table widely exists in various document images,and table detection is of great significance for the reuse of table information.In response to issues such as limited receptive field,reliance on predefined proposals,and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network,a table detection network based on DINO model is proposed in this paper.Firstly,an image preprocessing method is designed to enhance the corner and line features of table,enabling more precise table boundary localization and effective differentiation between table and other document elements like text.Secondly,a backbone network SwTNet-50 is designed,and Swin Transformer Blocks(STB)are introduced into ResNet to effectively combine local and global features,and the feature extraction ability of the model and the detection accuracy of table boundary are improved.Finally,to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model,a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision.Compared with various table detection methods based on deep learning,our model is better than other algorithms on the TNCR table detection dataset,with F1-Scores of 98.2％,97.4％,and 93.3％for IoU thresholds of 0.5,0.75,and 0.9,respectively.On the ⅢT-AR-13K dataset,the F1-Score is 98.6％when the IoU threshold is 0.5.
万方数据
维普
