Comparative study of the use of geometrical moments for Arabic handwriting recognition

Moments and functions of moments have been employed as pattern features in numerous applications to recognize two-dimensional image patterns. These pattern features extract global properties of the image such as the shape area, the center of the mass, the moment of inertia, and so on. This paper shows the use of different moments to extract features from offline Arabic words. Invariants moment of Hu, Zernike moments, Pseudo Zernike moments, Tchebichef moments, and Legendre moments have been applied to the IFN/ENIT database with a neural network classifier and the results have been compared. Our results show that pseudo Zernike moments yields the best recognition accuracy of 89%.