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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Tuesday 25 September 2018

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Printed Arabic Noisy Characters Recognition Using the Multi-layer Perceptron


Volume 9, Issue 1, September 2014, Pages 61–69

 Printed Arabic Noisy Characters Recognition Using the Multi-layer Perceptron

R. Salouan, S. Safi, and B. Bouikhalene

Original language: English

Received 23 June 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


In this paper, we present a comparison between two methods of features extraction; the first one is the Krawtchouk invariant moment (KIM). The second one is the Zernike invariant moment (ZIM). These moments are used for printed Arabic characters recognition in different situations: translated, rotated or resized and noisy. In the pre-processing phase we use the thresholding technique. In the learning-classification phase we use the multi-layer perceptron (MLP) that is considered as a neural network based on a supervised learning. The simulation result that we have obtained demonstrates that the KIM is more robust than ZIM in this recognition.

Author Keywords: The noisy printed Arabic characters, the thresholding technique, the Krawtchouk invariant moments, the Zernike invariant moments, the multi-layer perceptron.


How to Cite this Article


R. Salouan, S. Safi, and B. Bouikhalene, “Printed Arabic Noisy Characters Recognition Using the Multi-layer Perceptron,” International Journal of Innovation and Scientific Research, vol. 9, no. 1, pp. 61–69, September 2014.