— 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.
— 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, firstname.lastname@example.org).
Kozo Mizutani is with Teikyo University, Faculty of Science and Engineering, Japan (e-mail: email@example.com).
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.