Table-structure recognition method using neural networks for implicit ruled line estimation and cell estimation

Tables are often used to summarize accurate values in academic papers, while graphs are used to show them visually. Automatic graph generation from a table is therefore a topic of research interest. Given that the way tables are written varies depending on the author, in earlier work we proposed a cell-detection-based table-structure recognition method. Our method achieved fair performance in experiments using the ICDAR 2013 table competition dataset, but could not outperform the top-ranked participant in the competition. This paper proposes an improved method using two neural networks: one estimates implicit ruled lines that are necessary to separate cells but are undrawn, and the other estimates cells by merging detected tokens in a table. We demonstrated the effectiveness of the proposed method by experiments using the same ICDAR 2013 dataset. It achieved an F-measure of 0.955, thereby outperforming the other methods including the top-ranked participant.