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A consideration of writer identification using disentangled features that independent of character classes
Author(s): Tomoki Yamada; Mariko Hosoe; Kunihito Kato; Kazuhiko Yamamoto
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Paper Abstract

Writer identification is one of the active areas of research. It is important to prepare a large number of characters of the same class to improve the accuracy of writer identification. However, it is not always possible to prepare enough characters of the same class. In this case, handwriting examiners compare different classes of characters and analyze using common handwriting parts for each character. However, this is very difficult. Therefore, we assume that handwriting characters written by the same writer have features independent of character classes. In this paper, we propose methods to extract features that are independent of character classes using deep neural networks. We used Conditional Variational AutoEncoder (CVAE) as a learning method. A writer identification experiment shows that these methods can extract independent features of character classes, and extracted features are useful in writer identification. Furthermore, we examined the relationship between human interpretation of character features and accuracy of writer identification by using character features extracted by disentangled feature extraction methods.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490V (22 March 2019); doi: 10.1117/12.2521372
Show Author Affiliations
Tomoki Yamada, Gifu Univ. (Japan)
Mariko Hosoe, Gifu Prefectural Police Headquarters (Japan)
Kunihito Kato, Gifu Univ. (Japan)
Kazuhiko Yamamoto, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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