{"title":"基于HMM的OCR系统性能评价","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":"{\"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}","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}
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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