{"title":"Discrimination between printed and handwritten characters for cheque OCR system","authors":"Weiran Xu, Honggang Zhang, Jun Guo, Guang Chen","doi":"10.1109/ICMLC.2002.1174543","DOIUrl":null,"url":null,"abstract":"The identification of printed and handwritten characters is a fundamental and important issue for the cheque OCR system to achieve high-accuracy. In this paper, a novel method is presented to identify the written type based on only 4 or 5 characters in a severely corrupted bank cheque image. We first extract 4 kinds of features, totaling 17 features. Then the most suitable features are selected using the method based on separability measure. Finally, the selected features are used by a naive Bayesian classifier to realize the discrimination. Using 12,158 real checks to test our method, the accuracy is 99.2%.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"11 1","pages":"1048-1053 vol.2"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1174543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The identification of printed and handwritten characters is a fundamental and important issue for the cheque OCR system to achieve high-accuracy. In this paper, a novel method is presented to identify the written type based on only 4 or 5 characters in a severely corrupted bank cheque image. We first extract 4 kinds of features, totaling 17 features. Then the most suitable features are selected using the method based on separability measure. Finally, the selected features are used by a naive Bayesian classifier to realize the discrimination. Using 12,158 real checks to test our method, the accuracy is 99.2%.
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