{"title":"A geometric approach to machine-printed character recognition","authors":"Li Wang, T. Pavlidis","doi":"10.1109/CVPR.1992.223206","DOIUrl":null,"url":null,"abstract":"An approach to feature extraction that eliminates binarization by extracting features directly from gray scale images is presented. It not only allows the processing of poor quality input (e.g., low contrast, dirty images), but also offers the possibility of significantly lower resolution for digitization.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.1992.223206","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
An approach to feature extraction that eliminates binarization by extracting features directly from gray scale images is presented. It not only allows the processing of poor quality input (e.g., low contrast, dirty images), but also offers the possibility of significantly lower resolution for digitization.<>
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