Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Investigating better multi-layer perceptrons for the task of classification
WSEAS Transactions on Computers
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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.