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Editor-in-chief

 
Faculty of Science, University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2014 Vol.4(5): 331-335 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2014.V4.459

Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features

Ayumu Soma, Kozo Mizutani, and Masayuki Arai
Abstract— This paper presents a study on character features and recognizers used for writer identification of offline handwritten Kanji characters. It is shown that a combination of two global features, two local features, and majority voting as a recognizer is efficient for writer identification. We performed experiments using an offline Kanji character database containing one-hundred Kanji characters, each written by one-hundred writers, and fifty samples of each Kanji character for a given writer. The experimental results show that the identification rate is 7 points higher than the conventional method using a single feature and obtained an identification rate higher than 99% by using three character classes.

Index Terms— Multiple features, offline handwritten Kanji character, recognizer, writer identification.

Ayumu Soma and Masayuki Arai are with Graduate School of Science & Engineering, Teikyo University, 1-1 Toyosatodai, Utsunomiya, Tochigi, Japan (e-mail: 12M105@uccl.teikyo-u.ac.jp, arai@ics.teikyo-u.ac.jp).
Kozo Mizutani is with Teikyo University, Faculty of Science and Engineering, Japan (e-mail: mizutani@uccl.teikyo-u.ac.jp).

[PDF]

Cite: Ayumu Soma, Kozo Mizutani, and Masayuki Arai, " Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features," International Journal of Information and Electronics Engineering vol. 4, no. 5, pp. 331-335, 2014.

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