A classifier for Arabic handwritten characters based on supervised self-organizing map neural network
Abstract
In this research, It is first time that a supervised Self-Organizing Map (SOM) neural network is introduced as a classifier for Arabic handwriting. Classification has been achieved 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. It has been shown that the proposed method is more effective than the conventional matching methods used in OCR systems.
Publication Title
International Conference on Mathematical Models for Engineering Science - Proceedings
Recommended Citation
Ali, M. (2010). A classifier for Arabic handwritten characters based on supervised self-organizing map neural network. International Conference on Mathematical Models for Engineering Science - Proceedings, 185-190. Retrieved from https://digitalcommons.memphis.edu/facpubs/13354
