Table Structure Recognition Based on Grid Shape Graph
Eunji Lee, Jun-Hyeong Kwon, Haeyoon Yang, Jaewoo Park, Soonyoung Lee, Hyung Il Koo, Nam Ik Cho · 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · 2022
Since tables in documents provide important information in compact form, table understanding has been an essential topic in document image processing. Researchers represented table structures in various formats for table understanding, such as simple grid structure, a graph with text/cell boxes as nodes, or a sequence of HTML tokens. However, these approaches have difficulties in handling regularities, e.g., global row and column information, and spanning cells simultaneously. In this paper, we propose a new table recognition method based on a grid shape graph and present grid localization and grid elements grouping networks. This approach is designed to exploit the grid structure and deal with spanning cells. To convert grid structure into cell structure, we only have to test adjacent pairs of grid elements, enabling efficient inference. In addition, we have discovered that predicting row/column-based relationships between grid elements improve cell-based connectivity estimation performance. We demonstrate the effectiveness of the proposed method through experiments on three benchmark datasets.