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An Integrated Approach for Table Detection and Structure Recognition

Detecting and identifying the table structure is an important issue in document digitization. Although there have been many great strides based on current deep learning techniques, table structure identification is still a difficult and difficult problem, especially when solving the problem of digitizing text in practice. The paper proposes a solution to digitize table documents based on the Cascade R-CNN HRNet network to detect, classify tables and integrate image processing algorithms to improve table data identification results. The proposed algorithm proved effective on real data - the hydrometeorological station record book contains tables including simple and complex structures tables with over 98% accuracy.