ArSLAT: Arabic Sign Language Alphabets Translator

Abstract

This paper presents an automatic translation system for gestures of manual alphabets in the Arabic sign language. The proposed Arabic Sign Language Alphabets Translator (ArSLAT) system does not rely on using any gloves or visual markings to accomplish the recognition job. As an alternative, it deals with images of bare hands, which allows the user to interact with the system in a natural way. The proposed ArSLAT system consists of five main phases; pre-processing phase, best-frame detection phase, category detection phase, feature extraction phase, and classification phase. The used extracted features are translation, scale, and rotation invariant in order to make the system more flexible. Experiments revealed that the proposed ArSLAT system was able to recognize the Arabic alphabets with an accuracy of 91.3% and 83.7% using minimum distance classifier (MDC) and multilayer perceptron (MLP) classifier, respectively.