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Lexicon-based word recognition using support vector machine and Hidden Markov Model

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dc.citedby18
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorViard-Gaudin C.en_US
dc.contributor.authorKhalid M.en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid9133978000en_US
dc.contributor.authorid7101640051en_US
dc.date.accessioned2023-12-29T07:51:21Z
dc.date.available2023-12-29T07:51:21Z
dc.date.issued2009
dc.description.abstractHybrid of Neural Network (NN) and Hidden Markov Model (HMM) has been popular in word recognition, taking advantage of NN discriminative property and HMM representational capability. However, NN does not guarantee good generalization due to Empirical Risk minimization (ERM) principle that it uses. In our work, we focus on online word recognition using the support vector machine (SVM) for character recognition. SVM's use of structural risk minimization (SRM) principle has allowed simultaneous optimization of representational and discriminative capability of the character recognizer. We evaluated SVM in isolated character recognition environment using IRONOFF and UNIPEN character database. We then demonstrate the practical issues in using SVM within a hybrid setting with HMM for word recognition by testing the hybrid system on the IRONOFF word database and obtained commendable results. � 2009 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5277749
dc.identifier.doi10.1109/ICDAR.2009.248
dc.identifier.epage165
dc.identifier.scopus2-s2.0-71249127581
dc.identifier.spage161
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-71249127581&doi=10.1109%2fICDAR.2009.248&partnerID=40&md5=fcef44f1bfc15cd8b376e113ca7b3288
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30687
dc.pagecount4
dc.sourceScopus
dc.sourcetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.subjectCharacter recognition
dc.subjectHidden Markov models
dc.subjectHybrid systems
dc.subjectImage retrieval
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectVocabulary control
dc.subjectCharacter database
dc.subjectEmpirical risk minimization
dc.subjectPractical issues
dc.subjectSimultaneous optimization
dc.subjectStructural risk minimization principle
dc.subjectWord recognition
dc.subjectSupport vector machines
dc.titleLexicon-based word recognition using support vector machine and Hidden Markov Modelen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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