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Online handwriting recognition using support vector machine

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dc.citedby37
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorKhalid M.en_US
dc.contributor.authorViard-Gaudin C.en_US
dc.contributor.authorPoisson E.en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid7101640051en_US
dc.contributor.authorid9133978000en_US
dc.contributor.authorid12805591100en_US
dc.date.accessioned2023-12-28T08:58:04Z
dc.date.available2023-12-28T08:58:04Z
dc.date.issued2004
dc.description.abstractDiscrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN [3]. Support Vector Machine (SVM) is an alternative to NN. In speech recognition (SR), SVM has been successfully used in the context of a hybrid SVM/HMM system. It gives a better recognition result compared to the system based on hybrid NN/HMM[4]. This paper describes the work in developing a hybrid SVM/HMM OHR system. Some preliminary experimental results of using SVM with RBF kernel on IRONOFF, UNIPEN and IRONOFF-UNIPEN character database are provided. � 2004IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.epageA314
dc.identifier.scopus2-s2.0-27944459317
dc.identifier.spageA311
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-27944459317&partnerID=40&md5=b1d4a007e9b0c33a09b4fcb919cdd0b2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29886
dc.identifier.volumeA
dc.pagecount3
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Region 10 Annual International Conference, Proceedings/TENCON
dc.subjectDatabase systems
dc.subjectMarkov processes
dc.subjectNeural networks
dc.subjectSpeech recognition
dc.subjectWord processing
dc.subjectHidden Markov Models (HMM)
dc.subjectHybrid systems
dc.subjectSupport vector machines (SVM)
dc.subjectOnline systems
dc.titleOnline handwriting recognition using support vector machineen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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