TY - GEN
T1 - Manga Text Detection with Manga-Specific Data Augmentation and Its Applications on Emotion Analysis
AU - Yang, Yi Ting
AU - Chu, Wei Ta
N1 - Funding Information:
Acknowledgement. This work was funded in part by Qualcomm through a Taiwan University Research Collaboration Project and in part by the National Science and Technology Council, Taiwan, under grants 111-3114-8-006-002, 110-2221-E-006-127-MY3, 108-2221-E-006-227-MY3, 107-2923-E-006-009-MY3, and 110-2634-F-006-022.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - We especially target at detecting text in atypical font styles and in cluttered background for Japanese comics (manga). To enable the detection model to detect atypical text, we augment training data by the proposed manga-specific data augmentation. A generative adversarial network is developed to generate atypical text regions, which are then blended into manga pages to largely increase the volume and diversity of training data. We verify the importance of manga-specific data augmentation. Furthermore, with the help of manga text detection, we fuse global visual features and local text features to enable more accurate emotion analysis.
AB - We especially target at detecting text in atypical font styles and in cluttered background for Japanese comics (manga). To enable the detection model to detect atypical text, we augment training data by the proposed manga-specific data augmentation. A generative adversarial network is developed to generate atypical text regions, which are then blended into manga pages to largely increase the volume and diversity of training data. We verify the importance of manga-specific data augmentation. Furthermore, with the help of manga text detection, we fuse global visual features and local text features to enable more accurate emotion analysis.
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U2 - 10.1007/978-3-031-27818-1_3
DO - 10.1007/978-3-031-27818-1_3
M3 - Conference contribution
AN - SCOPUS:85152559481
SN - 9783031278174
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 40
BT - MultiMedia Modeling - 29th International Conference, MMM 2023, Proceedings
A2 - Dang-Nguyen, Duc-Tien
A2 - Gurrin, Cathal
A2 - Smeaton, Alan F.
A2 - Larson, Martha
A2 - Rudinac, Stevan
A2 - Dao, Minh-Son
A2 - Trattner, Christoph
A2 - Chen, Phoebe
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on MultiMedia Modeling, MMM 2023
Y2 - 9 January 2023 through 12 January 2023
ER -