Multi-Scale Feature Enhancement Based Method for Table Structure Segmentation

Automatic recognition of forms to efficiently utilize form data has become an important requirement in areas such as business and management. Accurately segmenting the table structure is a key step in the automatic table recognition technology. However, when facing complex forms with non-standard layouts, complex backgrounds, and distorted and fuzzy forms, the traditional table structure segmentation methods still have the problem of low accuracy. So to solve the problem, this study proposes a table structure segmentation method based on multi-scale feature enhancement. Firstly, we add a convolutional neural network (CNN) branch based on the framework of Swin-U net semantic segmentation model to extract form image features from three different resolution scales, namely, low, medium, and high; secondly, we introduce a Multi-Scale Feature Attention (MFA) mechanism in the branch of Swin-transformer to achieve the more accurate capture of the three-scale features; and finally, a multi-scale feature fusion module-CTFM is constructed to fuse the extracted features to enhance the segmentation ability of the model on the table structure. We trained our model on one of our own manually collected labeled complex table datasets (CWTD) and validated it with publicly available SciTSR and PubTabNet data, where our method achieves the best Dice_coef scores of 96.72% and 96.52% respectively, proving that our method has more accurate segmentation.