A Comparative Study Between the Hidden Markov Models and the Support Vector Machines for Noisy Printed Numerals Latin Recognition
R. Salouan, Saïd Safi, Belaid Bouikhalene, Beni Mellal · 2015
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.