Table-structure Recognition Method Consisting of Plural Neural Network Modules

Hiroyuki Aoyagi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

2022

Abstract

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 top-ranked participant in that competition by 2.6 percentage points.

Paper Citation

in Harvard Style

Aoyagi H., Kanazawa T., Takasu A., Uwano F. and Ohta M. (2022). Table-structure Recognition Method Consisting of Plural Neural Network Modules. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 542-549. DOI: 10.5220/0010817700003122

in Bibtex Style

@conference{icpram22,
author={Hiroyuki Aoyagi and Teruhito Kanazawa and Atsuhiro Takasu and Fumito Uwano and Manabu Ohta},
title={Table-structure Recognition Method Consisting of Plural Neural Network Modules},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={542-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010817700003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Table-structure Recognition Method Consisting of Plural Neural Network Modules
SN - 978-989-758-549-4
AU - Aoyagi H.
AU - Kanazawa T.
AU - Takasu A.
AU - Uwano F.
AU - Ohta M.
PY - 2022
SP - 542
EP - 549
DO - 10.5220/0010817700003122