TY - GEN
T1 - Table-structure Recognition Method Consisting of Plural Neural Network Modules
AU - Aoyagi, Hiroyuki
AU - Kanazawa, Teruhito
AU - Takasu, Atsuhiro
AU - Uwano, Fumito
AU - Ohta, Manabu
N1 - Publisher Copyright:
© 2022 by SCITEPRESS – Science and Technology Publications, Lda.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Neural Network
KW - PDF
KW - Table-structure Recognition
KW - XML
UR - https://www.scopus.com/pages/publications/85174604722
UR - https://www.scopus.com/pages/publications/85174604722#tab=citedBy
U2 - 10.5220/0010817700003122
DO - 10.5220/0010817700003122
M3 - Conference contribution
AN - SCOPUS:85174604722
SN - 9789897585494
T3 - International Conference on Pattern Recognition Applications and Methods
SP - 542
EP - 549
BT - ICPRAM 2022 - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods, Volume 1
A2 - De Marsico, Maria
A2 - Sanniti di Baja, Gabriella
A2 - Fred, Ana L.N.
PB - Science and Technology Publications, Lda
T2 - 11th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2022
Y2 - 3 February 2022 through 5 February 2022
ER -