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...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Conference paper |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-29886 |
---|---|
record_format |
dspace |
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 |
13.214268 |