Volume 9, Issue 1, September 2014, Pages 61–69
R. Salouan1, S. Safi2, and B. Bouikhalene3
1 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
2 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
3 Polydisciplinary Faculty, University Sultan Moulay Slimane, Morocco
Original language: English
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.
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.
R. Salouan1, S. Safi2, and B. Bouikhalene3
1 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
2 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
3 Polydisciplinary Faculty, University Sultan Moulay Slimane, Morocco
Original language: English
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.
