RECOGNITION OF ARABIC CHARACTERS

The subject of character recognition has been receiving considerable attention in recent years due to the increasing dependence on computer data processing. Several methods for recognizing Latin, Chinese, and Kanji characters have been proposed. However, work on recognition of Arabic characters has been relatively sparse. Techniques developed for classifying characters in other languages cannot be used for recognizing Arabic characters due to the differences in structure. The shape of an arabic character is a function of its location within a word, where each character can have two to four different forms. Most of the techniques proposed to date for recognizing Arabic characters have relied on structural and topographic approaches. This paper introduces a statistical approach for the recognition of Arabic characters. As a first step, the character is segmented into primary and secondary parts (dots and zigzags). The secondary parts of the character are then isolated and identified separately, thereby reducing the number of classes from 28 to 18. The moments of the horizontal and vertical projections of the remaining primary characters are then calculated and normalized with respect to the zero order moment. Simple measures of shape are obtained from the normalized moments. A 9-D feature vector is obtained for each character. Classification is accomplished using quadratic discriminant functions. The approach was evaluated using isolated, handwritten, and printed characters from a database established for this purpose. The results indicate that the technique offers better classification rates in comparison with existing methods.