Optical Character Recognition of Arabic handwritten characters using Neural Network

Optical Character Recognition (OCR) is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used as a form of data entry. This paper proposes an approach to design and implement an off-line OCR system that recognizes Arabic handwritten characters; in this approach Artificial Neural Networks (ANNs) were used as classifiers. The ANN was trained based on the Hopfield Algorithm which was designed using MATLAB. In our system, the image goes through a preprocessing stage, followed by a features extraction stage and a recognition stage. For the recognition to be accurate certain properties of each of the letters are calculated, these properties also called features are extracted from the image. Selection of a relevant feature extraction method is probably the single most important factor in achieving high recognition performance with much better accuracy in character recognition systems. A collection of such features (vectors) define the character uniquely by the means of an ANN. Experimental results showed that the system designed is able to recognize eight Arabic handwritten letters with a successful recognition rate of (77.25). The system designed can be further developed to include the rest of the Arabic Alphabets, and a segmentation stage so that it could recognize words.