Volume 9, Issue 3, November 2014, Pages 1032–1042
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.
This research presents a comparison between two methods of learning-classification, the first one is a probabilistic and unsupervised which is the Hidden Model Markov (HMM), while the second is a statistic and supervised that is the Support Vectors Machine (SVM). These techniques are used for printed Eastern Arabic numerals recognition, in different situation: rotated and translated or resized and noisy. In the pre-processing phase we have used the thresholding technique while in the features extraction we exploited the Krawtchouk Invariant Moment (KIM). In fact, in order to make a precise comparison between these two methods, we have introduced two new concepts which are the threshold and the interval of stability of each numeral and for each of these two methods. The simulation results that we obtained demonstrates that SVM is more performing than the HMM technique in this recognition system.
Author Keywords: The noisy printed Eastern Arabic numerals, the thresholding technique, The Krawtchouk invariant moment, the hidden Markov models, the support vectors machines.
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
This research presents a comparison between two methods of learning-classification, the first one is a probabilistic and unsupervised which is the Hidden Model Markov (HMM), while the second is a statistic and supervised that is the Support Vectors Machine (SVM). These techniques are used for printed Eastern Arabic numerals recognition, in different situation: rotated and translated or resized and noisy. In the pre-processing phase we have used the thresholding technique while in the features extraction we exploited the Krawtchouk Invariant Moment (KIM). In fact, in order to make a precise comparison between these two methods, we have introduced two new concepts which are the threshold and the interval of stability of each numeral and for each of these two methods. The simulation results that we obtained demonstrates that SVM is more performing than the HMM technique in this recognition system.
Author Keywords: The noisy printed Eastern Arabic numerals, the thresholding technique, The Krawtchouk invariant moment, the hidden Markov models, the support vectors machines.
How to Cite this Article
R. Salouan, S. Safi, and B. Bouikhalene, “Printed Eastern Arabic Noisy Numerals Recognition Using Hidden Markov Model and Support Vectors Machine,” International Journal of Innovation and Applied Studies, vol. 9, no. 3, pp. 1032–1042, November 2014.
