Arabic Handwritten Alphanumeric Character Recognition using Fuzzy Attributed Turning Functions

In this paper, we present a novel method for recognition of unconstrained handwritten Arabic alphanumeric characters. The algorithm binarizes the character image, smoothes it and extracts its contour. A novel approach for polygonal approximation of handwritten character contours is applied. The directions and length features are extracted from the polygonal approximation. These features are used to build character models in the training phase. For the recognition purpose, we introduce Fuzzy Attributed Turning Functions (FATF) and define a dissimilarity measure based on FATF for comparing polygonal shapes. Experimental results demonstrate the effectiveness of our algorithm for recognition of handwritten Arabic characters. We have obtained around 98% accuracy for Arabic handwritten characters and more than 97% accuracy for handwritten Arabic numerals.