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Efficient Genetic Algorithms for Arabic Handwritten Characters Recognition | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 13, Volume 6, Issue 2, 2009, Pages 137-157 PDF (486.88 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2009.163804 | ||
| Author | ||
| Laheeb Mohammad Ibrahim | ||
| College of Computer Sciences and Mathematics University of Mosul, Iraq | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Arabic handwritten characters recognition; Genetic Algorithm (GA); Feature Extracted; Feature selection; Hopfield Neural Network | ||
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