An effective method for figures and tables detection in academic literature

Abstract

Figures and tables in scientific articles serve as data sources for various academic data mining tasks. These tasks require input data to be in its entirety. However, existing studies measure the performance of algorithms using the same IoU (Intersection over Union) or IoU-based metrics that are used for natural situations. There is a gap between high IoU and detection entirety in scientific figures and tables detection tasks. In this paper, we demonstrate the existence of this gap and suggest that the leading cause is the detection error in the boundary area. We propose an effective detection method that cascades semantic segmentation and contour detection. The semantic segmentation model adopted a novel loss function to enhance the weights of boundary parts and a categorized dice metric to evaluate the imbalanced pixels in the segmentation result. Under rigorous testing criteria, the method proposed in this paper yielded a page-level F1 of 0.983 exceeding state-of-the-art academic figure and table detection methods. The research results in this paper can significantly improve the data quality and reduce data cleaning costs for downstream applications.