Towards Arabic Alphabet and Numbers Sign Language Recognition

Abstract

This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann Machines and a direct use of tiny images Restricted Boltzmann Machines are able to code images as a superposition of a limited number of features taken from a larger alphabet Repeating this process in deep architecture Deep Belief Networks leads to an efficient sparse representation of the initial data in the feature space A complex problem of classification in the input space is thus transformed into an easier one in the feature space After appropriate coding a softmax regression in the feature space must be sufficient to recognize a hand sign according to the input image To our knowledge this is the first attempt that tiny images feature extraction using deep architecture is a simpler alternative approach for Arabic sign language recognition that deserves to be considered and investigated