Online handwriting recognition using support vector machine

Discrete 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 s...

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Main Authors: Ahmad A.R., Khalid M., Viard-Gaudin C., Poisson E.
Other Authors: 35589598800
Format: Conference paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-298862023-12-28T16:58:04Z Online handwriting recognition using support vector machine Ahmad A.R. Khalid M. Viard-Gaudin C. Poisson E. 35589598800 7101640051 9133978000 12805591100 Database systems Markov processes Neural networks Speech recognition Word processing Hidden Markov Models (HMM) Hybrid systems Support vector machines (SVM) Online systems Discrete 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. Final 2023-12-28T08:58:04Z 2023-12-28T08:58:04Z 2004 Conference paper 2-s2.0-27944459317 https://www.scopus.com/inward/record.uri?eid=2-s2.0-27944459317&partnerID=40&md5=b1d4a007e9b0c33a09b4fcb919cdd0b2 https://irepository.uniten.edu.my/handle/123456789/29886 A A311 A314 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Database systems
Markov processes
Neural networks
Speech recognition
Word processing
Hidden Markov Models (HMM)
Hybrid systems
Support vector machines (SVM)
Online systems
spellingShingle Database systems
Markov processes
Neural networks
Speech recognition
Word processing
Hidden Markov Models (HMM)
Hybrid systems
Support vector machines (SVM)
Online systems
Ahmad A.R.
Khalid M.
Viard-Gaudin C.
Poisson E.
Online handwriting recognition using support vector machine
description Discrete 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.
author2 35589598800
author_facet 35589598800
Ahmad A.R.
Khalid M.
Viard-Gaudin C.
Poisson E.
format Conference paper
author Ahmad A.R.
Khalid M.
Viard-Gaudin C.
Poisson E.
author_sort Ahmad A.R.
title Online handwriting recognition using support vector machine
title_short Online handwriting recognition using support vector machine
title_full Online handwriting recognition using support vector machine
title_fullStr Online handwriting recognition using support vector machine
title_full_unstemmed Online handwriting recognition using support vector machine
title_sort online handwriting recognition using support vector machine
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426307521675264
score 13.188404