LocRecNet: A Synergistic Framework for Table Localization and Rectification

This paper introduces LocRecNet, a deformation-aware network for table localization and correction, aimed at improving the recognition accuracy of complex table data. Conventional algorithms typically depend on table cell or line features for model training but exhibit limitations when processing real-world deformed table data. LocRecNet addresses these challenges by correcting deformations prior to table structure recognition, significantly enhancing model performance. The proposed network employs a novel keypoint detection method to precisely locate table edge points, enabling the efficient correction of deformed tables. Experimental results reveal that integrating LocRecNet substantially improves table recognition algorithms in terms of various key performance metrics, with recall rates increasing by up to 10% and F1 scores nearing 90%. Tests conducted on real-world datasets further validate its effectiveness, demonstrating a reasonable trade-off between computational cost and performance gains. Additionally, LocRecNet enhances performance even on standard table data, highlighting its strong generalizability and potential for broader application.