Research on Table Recognition Algorithms Based on Semantic Interpretation and Structural Parsing

Table recognition and parsing is a crucial task in the field of information extraction. With the rapid development of deeplearning, significant progress has been made in table recognition algorithms based on image processing and natural languageprocessing. However, challenges still remain in terms of table styles, table contents, algorithm flexibility, and applicability. Toaddress these issues, an end-to-end table recognition algorithm based on semantic understanding and structural parsing isproposed. By processing textual semantics and structure, dynamic interpolation detection, overlap-driven algorithms, anddifferential methods are employed to achieve general table structure recognition and content filling. Experimental results showthat the algorithm achieves a structure and content recognition accuracy of 99.85% on the dataset, and demonstrates excellentrecognition performance on hand-drawn tables, especially showing strong robustness in complex border-line scenarios. Thisresearch aims to provide new theoretical support and technical references for the table recognition field.