Arabic handwritten characters classification using learning vector quantization algorithm
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. © 2008 Springer-Verlag.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Ali, M. (2008). Arabic handwritten characters classification using learning vector quantization algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 463-470. https://doi.org/10.1007/978-3-540-69905-7_53
