Abstract:

Traditional methods for identifying table structures are difficult to fully learn complex table structures such as merge cells with multiple rows and columns, blank cells, nested cells, and are lack of information in the process of extracting features. An OCR(Optical Character Recognition) table structure identification method based on improved SLANet (Structure Location Alignment Network) is proposed. Firstly, the lightweight CPU (Central Processing Unit) convolutional neural network is used and attention mechanism is introduced to enhance the generalization ability and explanation ability of the network. The information vector obtained by training is

inputed into the lightweight high-low level feature fusion module to extract features, and then the outputted features are aligned with the structure and position information through the feature decoded module to obtain the prediction label. Experiments show that compared to EDD ( Encoder-Dual-Decoder), TableMaster and other models, the accuracy of the proposed method has been significantly improved, reaching 76. 95% , and the TEDS (Tree-Edit-Distance-based Similarity) has reached 95. 57% , which significantly enhances the model’s ability to identify complex table structures and provides an optimization strategy for identifying table structures.

Key words: recognition table structure, structure location alignment network( SLANet), attention mechanism, tree-edit-distance-based similarity(TEDS)