Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm
Identifieur interne : 000C73 ( Main/Exploration ); précédent : 000C72; suivant : 000C74Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm
Auteurs : A. Ali [Libye]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwritten character. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers for three subsets of 53 Arabic CBSs, the three subsets of Arabic CBSs are; ascending CBSs, descending CBSs and embedded CBSs. Three training algorithms; OLVQ1, LVQ2 and LVQ3 were examined and OLVQ1 found as the best learning algorithm.
Url:
DOI: 10.1007/978-3-540-69905-7_53
Affiliations:
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