{"title":"Recognition of handprinted Chinese characters using Gabor features","authors":"Y. Hamamoto, S. Uchimura, K. Masamizu, S. Tomita","doi":"10.1109/ICDAR.1995.602027","DOIUrl":null,"url":null,"abstract":"A method for handprinted Chinese character recognition based on Gabor filters is proposed. The Gabor approach to character recognition is intuitively appealing because it is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. The performance of a character recognition system using Gabor features is demonstrated on the ETL-8 character set. Mental results show that the Gabor features yielded an error rate of 2.4% versus the error rate of 4.4% obtained by using a popular feature extraction method.","PeriodicalId":273519,"journal":{"name":"Proceedings of 3rd International Conference on Document Analysis and Recognition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 3rd International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.1995.602027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
A method for handprinted Chinese character recognition based on Gabor filters is proposed. The Gabor approach to character recognition is intuitively appealing because it is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. The performance of a character recognition system using Gabor features is demonstrated on the ETL-8 character set. Mental results show that the Gabor features yielded an error rate of 2.4% versus the error rate of 4.4% obtained by using a popular feature extraction method.
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