Title :
Online handwriting recognition using support vector machine
Author :
Ahmad, Abdul Rahim ; Khalia, M. ; Viard-gaudin, Christian ; Poisson, Emilie
Author_Institution :
Univ. Tenaga Nasional, Selangor, Malaysia
Abstract :
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 [Y. Bengio et al., 1995]. 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 [A. Ganapathiraju, January 2002]. 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.
Keywords :
handwriting recognition; hidden Markov models; radial basis function networks; support vector machines; discrete hidden Markov model; handwritten word recognition system; neural network; online handwriting recognition; speech recognition; support vector machine; Databases; Handwriting recognition; Hidden Markov models; Neural networks; Personal digital assistants; Speech recognition; Strontium; Support vector machine classification; Support vector machines; Writing;
Conference_Titel :
TENCON 2004. 2004 IEEE Region 10 Conference
Print_ISBN :
0-7803-8560-8
DOI :
10.1109/TENCON.2004.1414419