  The work presents a comparative assessment of seven different feature sets
for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
(MLP) based classifier. The seven feature sets employed here consist of shadow
features, octant centroids, longest runs, angular distances, effective spans,
dynamic centers of gravity, and some of their combinations. On experimentation
with a database of 3000 samples, the maximum recognition rate of 95.80% is
observed with both of two separate combinations of features. One of these
combinations consists of shadow and centriod features, i. e. 88 features in
all, and the other shadow, centroid and longest run features, i. e. 124
features in all. Out of these two, the former combination having a smaller
number of features is finally considered effective for applications related to
Optical Character Recognition (OCR) of handwritten Arabic numerals. The work
can also be extended to include OCR of handwritten characters of Arabic
alphabet.
