New Developments and Applications of Self-Organizing Maps
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Supervised Dimensionality Reduction Method for Image Data Using Evolutionary Strategy
ICCRD '10 Proceedings of the 2010 Second International Conference on Computer Research and Development
On image matrix based feature extraction algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.