Enhancement of Moment Invariants calculation for Arabic Handwriting recognition

Moment Invariant (MI) has been frequently used as feature for shape recognition. These features are invariant to several deformations such as rotation, scaling and translation. However it is sensitive to distortions that primarily affect the `centre of gravity´ of the image. Images of an Arabic Word might have different centroid due to the fact that it might be written using different Handwriting styles. In this paper we examine the effect of replacing the image centroid with the center of image as the reference point in Moment Invariant (MI). The new descriptors set was tested to recognize Arabic Words based on IFN/ENIT Database that consisting of 26459 words written by 411 different writers. The Back Propagation Neural Network was used as the classifier. Experiment results had shown that by using the new descriptors the average recognition accuracy has increased by 18.38%.