Volume 5, Issue 1, July 2014, Pages 16–24
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 methods of learning-classification, the first one is called Hidden Model Markov (HMM) which is based on a unsupervised learning, and the second one called Support Vector Machine (SVM) which is based on a supervised learning. Those techniques are used for printed Latin numerals recognition, in different situation: rotated, resized and noisy. In the pre-processing phase we use the thresholding technic and in the features extraction we use the Hu invariants moments (HIM). The simulation results demonstrate that SVM is more robust than the HMM technic in the printed Latin numerals recognition.
Author Keywords: The noisy printed Latin numerals, the thresholding technique, the Hu invariant moments, the Hidden Model Markov, the Support Vector Machine.
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 methods of learning-classification, the first one is called Hidden Model Markov (HMM) which is based on a unsupervised learning, and the second one called Support Vector Machine (SVM) which is based on a supervised learning. Those techniques are used for printed Latin numerals recognition, in different situation: rotated, resized and noisy. In the pre-processing phase we use the thresholding technic and in the features extraction we use the Hu invariants moments (HIM). The simulation results demonstrate that SVM is more robust than the HMM technic in the printed Latin numerals recognition.
Author Keywords: The noisy printed Latin numerals, the thresholding technique, the Hu invariant moments, the Hidden Model Markov, the Support Vector Machine.
How to Cite this Article
R. Salouan, S. Safi, and B. Bouikhalene, “A Comparative Study Between the Hidden Markov Models and the Support Vector Machines for Noisy Printed Numerals Latin Recognition,” International Journal of Innovation and Scientific Research, vol. 5, no. 1, pp. 16–24, July 2014.
