Cursive Handwritten Word Recognition by Integrating Multiple Classifiers
Identifieur interne : 002106 ( Main/Merge ); précédent : 002105; suivant : 002107Cursive Handwritten Word Recognition by Integrating Multiple Classifiers
Auteurs : Kenichi Maruyama [Japon] ; Makoto Kobayashi [Japon] ; Yasuaki Nakano [Japon] ; Hirobumi Yamada [Japon]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1999.
Abstract
Abstract: This paper proposes a methodf or cursive handwritten word recognition. In the traditional research, many cursive handwritten word recognition systems useda single methodfor character recognition. In this research, we propose a methodi ntegrating multiple character classifier to improve wordre cognition rate combining the results of them. As a result of the experiment using two classifiers, wordre cognition rate is improvedt han from those using a single character classifier.
Url:
DOI: 10.1007/3-540-48172-9_12
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