Learning vector quantization algorithm as classifier for Arabic handwritten characters recognition

Authors:
Mohamed A. Ali;Kasmiran Bin Jumari;Salina Abd Samad
Affiliations:
Computer Department, Faculty of Science, Fakulti Kejuruteraan, Sebha University, Universiti Kebangsaan Malaysia, Libya, Malaysia;Elec., Electronics & System Engineering Department, Faculty of Science, Fakulti Kejuruteraan, Sebha University, Universiti Kebangsaan Malaysia, Libya, Malaysia;Elec., Electronics & System Engineering Department, Faculty of Science, Fakulti Kejuruteraan, Sebha University, Universiti Kebangsaan Malaysia, Libya, Malaysia
Venue:
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Year:
2007

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Abstract

In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwriting. 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 and 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.