This paper addresses the identification of Arabic handwritten digits. In addition to digit identifiability, the paper presents digit recognition. The digit image is divided into grids based on the distribution of the black pixels in the image. Several types of features are extracted (viz. gradient, curvature, density, horizontal and vertical run lengths, stroke, and concavity features) from the grid segments. K-Nearest Neighbor and Nearest Mean classifiers are used. A database of 70000 of Arabic handwritten digit samples written by 700 writers is used in the analysis and experimentations.
The identifiability of isolated and combined digits are tested. The analysis of the results indicates that Arabic digits 3 (٣), 4 (٤), 8 (٨), and 9 (٩) are more identifiable than other digits while Arabic digit 0 (٠) and 1 (١) are the least identifiable. In addition, the paper shows that combining the writer’s digits increases the discriminability power of Arabic handwritten digits. Combining the features of all digits, K-NN provided the best accuracy in text-independent writer identification with top-1 result of 88.14%, top-5 result of 94.81%, and top-10 results of 96.48%
