A Comparison between the Self-Organizing Maps and the Support Vector Machines for Handwritten Latin Numerals Recognition
R. Salouan, Saïd Safi, Belaid Bouikhalene, Department Of · 2015
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.