Efficient Genetic Algorithms for Arabic Handwritten Characters Recognition

The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Genetic algorithm is probably the most efficient method available for character recognition. In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of features and a validity index that measures the quality of clusters have been used to guide the search towards the more discriminate features and the best number of clusters.

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