Abstract
Arabic characters illustrate intricate, multidimensional and cursive visual information. Developing a machine learning system for Arabic character recognition is an exciting research. This paper addresses a neural computing concept for Arabic Optical Character Recognition (OCR). The method is based on local image sampling of each character to a selected feature matrix and feeding these matrices into a Bidirectional Associative Memory followed by Multilayer Perceptron (BAMMLP) with back propagation learning algorithm. The efficacy of the system has been justified over different test patterns of Arabic characters. Experimental results validate that the system is well efficient to recognize Arabic characters with overall more than 82% accuracy.