Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features
Keywords:
Multiple features, offline handwritten Kanji character, recognizer, writer identification.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.
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