Efficient genetic algorithms for arabic handwritten characters recognition

Abstract

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. The proposed strategy is evaluated synthetic data sets and then it is applied to Arabic handwritten characters recognition. Comprehensive experiments demonstrate the feasibility and efficiency of the proposed methodology, and show that Genetic Algorithm (GA) are applied here to improve the recognition speed as well as the recognition accuracy.