Table-structure Recognition Method Consisting of Plural Neural Network Modules

In academic papers, tables are often used to summarize experimental results. However, graphs are more suitable than tables for grasping many experimental results at a glance because of the high visibility. Therefore, automatic graph generation from a table has been studied. Because the structure and style of a table vary depending on the authors, this paper proposes a table-structure recognition method using plural neural network(NN) modules. The proposed method consists of four NN modules: two of them merge detected tokens in a table, one estimates implicit ruled lines that are necessary to separate cells but undrawn, and the last estimates cells by merging the tokens. We demonstrated the effectiveness of the proposed method by experiments using the ICDAR 2013 table competition dataset. Consequently, the proposed method achieved an F-measure of 0.972, outperforming those of our earlier work (Ohta et al., 2021) by 1.7 percentage points and of the topranked participant in that competition by 2.6 percentage points.