{"title":"Performance evaluation of an HMM based OCR system","authors":"J. C. Anigbogu, A. Belaïd","doi":"10.1109/ICPR.1992.201697","DOIUrl":null,"url":null,"abstract":"Presents a performance analysis of a first order hidden Markov model based OCR system. Trade-offs between accuracy in terms of recognition rates and complexity in terms of the number of states in the model are discussed. For most fonts, optimal performance is achieved with 6-state models. With adequate heuristics and reliable post-processors, 5-state and even 4-state models give reasonable performances (up to 99.60% at 4-states).<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 8
Abstract
Presents a performance analysis of a first order hidden Markov model based OCR system. Trade-offs between accuracy in terms of recognition rates and complexity in terms of the number of states in the model are discussed. For most fonts, optimal performance is achieved with 6-state models. With adequate heuristics and reliable post-processors, 5-state and even 4-state models give reasonable performances (up to 99.60% at 4-states).<>
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