I El-Fegh, Zakaria Suliman Zubi, Ali A Elrowayati, Faraj A El-Mouadib
Abstract
This paper presents the application of
Multi Layer Perceptron (MLP) Artificial Neural
Network to classification of handwritten Arabic
words. Zernik Moments are used as a feature vector
for each word. An efficient way to select the most
suitable order of Zernik moments is also presented.
The MLP is trained in a supervised fashion using
the Back Propagation learning algorithm. Having
being trained, the MLP is tested on different set of
handwritten Arabic words that has never been seen
by the MLP. Several experiments are performed to
select the best MLP structure. Experimental results
have shown that with the presented structure and
the order of the Zernik Moments more than 87% of
correct recognition was obtained.
Publication Date
3/23/2009
Publisher
Proceedings of the 10th WSEAS international conference on evolutionary computing
علي عبدالحفيظ الروياتي | ALI ABDULHAFID ELROWAYATI | 5122