Mr. Tomoki Yamada Profile

Tomoki Yamada

at Gifu Univ

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Proceedings Article | 22 March 2019 Paper

A consideration of writer identification using disentangled features that independent of character classes

Tomoki Yamada, Mariko Hosoe, Kunihito Kato, Kazuhiko Yamamoto

Proceedings Volume 11049, 110490V (2019) https://doi.org/10.1117/12.2521372

KEYWORDS: Data modeling, Feature extraction, Neural networks, Analytical research, Convolution, Visualization, Forensic science, Computer programming

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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.

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