Volume 7, Issue 1, August 2014, Pages 50–56
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 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, 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 for learning-classification; the first one is called Kohonen network or Self-Organizing Maps (SOM) which is characterized by an unsupervised learning. The second one is called Support Vector Machine (SVM) which is based on a supervised learning. These techniques are used for recognition of handwritten Latin numerals that's extracted from MNIST database. In the pre-processing phase we use the thresholding, centering and skeletization techniques in the features extraction we use the zoning method. The simulation result demonstrates that the SVM is more robust than the SOM method in the recognition of handwritten numerals Latin.
Author Keywords: Handwritten Latin numerals, Thresholding, Centering, Skeletization techniques, Self-Organizing Maps (SOM), Support Vectors Machines (SVM).
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 Department of Mathematic and Informatic, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, 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 for learning-classification; the first one is called Kohonen network or Self-Organizing Maps (SOM) which is characterized by an unsupervised learning. The second one is called Support Vector Machine (SVM) which is based on a supervised learning. These techniques are used for recognition of handwritten Latin numerals that's extracted from MNIST database. In the pre-processing phase we use the thresholding, centering and skeletization techniques in the features extraction we use the zoning method. The simulation result demonstrates that the SVM is more robust than the SOM method in the recognition of handwritten numerals Latin.
Author Keywords: Handwritten Latin numerals, Thresholding, Centering, Skeletization techniques, Self-Organizing Maps (SOM), Support Vectors Machines (SVM).
How to Cite this Article
R. Salouan, S. SAFI, and B. BOUIKHALENE, “A Comparison between the Self-Organizing Maps and the Support Vector Machines for Handwritten Latin Numerals Recognition,” International Journal of Innovation and Scientific Research, vol. 7, no. 1, pp. 50–56, August 2014.
