title: A method of neighbor classes based SVM classification for optical printed Chinese character recognition.
abstract: In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.
authors.affiliation: Image Information Institute, School of Electronics and Information Engineering, Sichuan University, Chengdu, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Zhang
authors.forename: Xiaohong
authors.initials: X
authors.lastname: Wu
authors.forename: Yanmei
authors.initials: Y
authors.lastname: Yu
authors.forename: Daisheng
authors.initials: D
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D002681:China; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007802:Language; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D060388:Support Vector Machine; D014956:Writing
references.citation: IEEE Trans Pattern Anal Mach Intell. 2007 Aug;29(8):1465-9
references.pmid: 17568148
references.citation: IEEE Trans Pattern Anal Mach Intell. 1987 Jan;9(1):149-53
references.pmid: 21869386