Use an Efficient Neural Network to Improve the Arabic Handwriting Recognition

Author:

Al Hamad Husam Ahmed1

Affiliation:

1. Department of Information Technology College of Computer, Qassim University Qassim, Saudi Arabia

Abstract

Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.

Publisher

North Atlantic University Union (NAUN)

Reference30 articles.

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3. Jayanta Kumar Basu, Debnath Bhattacharyya, Tai-hoon Kim, “Use of Artificial Neural Network in Pattern Recognition”, International Journal of Software Engineering and Its Applications, vol. 4, No. 2, pp. 24-32, 2010.

4. Rosenblatt, F., “The Perceptron: A Probalistic Model For Information Storage and Organization In The Brain”. Psychological Review, vol. 65, pp. 386-408, 1958.

5. Fukushima, Kunihiko, “Cognitron: A self-organizing multilayered neural network”, Biological Cybernetics vol. 20(3-4), pp. 121–136, 1975.

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