Isolated Arabic Hand Written Letters Recognition Based on Contour Matching and Neural Network

Complexity of Arabic writing language makes its handwritten recognition very complex in terms of computer algorithms comparing with other languages such as English and French. The Arabic handwritten recognition has high importance in modern applications. The contour analysis of word image can extract special contour features that discriminate one character from another by the mean of vector features. This paper aims to implements a set of pre-processing functions over a handwritten Arabic character, with contour analysis, to enter the contour vector to neural network to recognize it. For training part, the neural network architecture was trained using many patterns regardless of the Arabic font style building a rigid recognition model. The presented algorithm structure got recognition ratio about 97%.