Ahmad, A. R. and Viard-Gaudin, C. and Khalid, M. (2009) Lexicon-based word recognition using support vector machine and hidden markov model. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. Institute of Electrical and Electronics Engineers, New York, pp. 161-165. ISBN 978-076953725-2
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Official URL: http://dx.doi.org/10.1109/ICDAR.2009.248
Hybrid 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.
|Item Type:||Book Section|
|Additional Information:||ISBN: 978-076953725-2; ICDAR2009 - 10th International Conference on Document Analysis and Recognition; Barcelona; 26 July 2009 through 29 July 2009|
|Uncontrolled Keywords:||character database, empirical risk minimization, practical issues, simultaneous optimization, structural risk minimization principle, word recognition|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Deposited By:||Liza Porijo|
|Deposited On:||07 Jul 2011 04:31|
|Last Modified:||07 Jul 2011 04:31|
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