Efficient Genetic Algorithms for Arabic Handwritten Characters Recognition

• Published in: AL-Rafidain journal of computer sciences and mathematics Vol. 6; no. 2; pp. 137 - 157
• Main Author: Ibrahim, Laheeb
• Format: Journal Article
• Language: Arabic; English
• Published: Mosul University 01-07-2009
• Subjects: arabic handwritten characters recognition; feature extracted; feature selection; genetic algorithm (ga); hopfield neural network
• ISSN: 2311-7990; 1815-4816; 2311-7990
• DOI: 10.33899/csmj.2009.163804

The main challenge in Arabic handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time high recognition rate. Due to its low computational requirement, genetic algorithm is probably the most efficient method available for character recognition. In this research we use objective of 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, and use Hopfield Neural Network as recognizer.             In this research Arabic handwritten characters recognition is applied. Experiments show the efficiency and flexibility of the proposed system, and show that Genetic Algorithm (GA) and Hopfield neural network are applied here to improve the recognition accuracy and make the recognition operation faster.