Robust Table Structure Recognition Network Based on Local and Global Perspectives
Liangliang Song, Xiang Huang, Shuyi Zhuang, Aodong Shen, Peidong Xu · IEEE Access · 2024
Table Structure Recognition (TSR) is pivotal in document analysis, especially in end-to-end automated identification, which profoundly impacts decision-making and efficiency spanning diverse fields such as power industry, education, healthcare, and scientific papers. However, accurately identifying table structures remains challenging due to complex layouts and irregular distortions. Considering the complex structure of tables in power relay protection systems, this study proposes a novel end-to-end TSR model, leveraging deep learning and innovative modules to address these challenges. Specifically, the new model TSR-Net replaces YOLOv8’s backbone with our proposed DSConv-Variants, improving accuracy and reducing parameters. Additionally, a Multi-Scale Attention Module (MAM) captures semantic features efficiently, while a Dynamic Calibration Module (DCM) enhances cross-layer fusion. Experimental results demonstrate the superior performance of our approach on public benchmarks, showcasing its utility for TSR.