This paper presents results obtained by applying two neural networks
models Backpropagation (BP), and Self-Organized Feature Map (SOFM) to a new
application of handwritten Arabic alphanumeric character (HAAC) recognition. A
novel method for features extraction, based on a shadow projection is used. Both
networks are trained using Arabic character samples written by different people
(learning set). They are required, after the learning is over, to recognize characters
out of the learning set. Evaluation of the recognition (classification) capability of
the two models for 28 alphanumeric characters is achieved. Depending on the
experimental results, a comparison of both algorithms is done.