Biocomp 83: Cambridge, UK, 26–29 September 1983
W. Greer · Journal of Biomedical Engineering · 1984
Automation over sign language recognition systems can greatly facilitate the vocal and the non-vocal communities which can be equivalently best and successive as speech-recognition systems. It offers enhancement of communication capabilities for the speech and hearing impaired, promising improved social opportunities and integration. This paper describes a gesture classification scheme which can classify a wide class of hand gesture in a view based setup. Since the images are from single camera view, it seems to be hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs fusion technique for three classifiers namely KNN, MLP and SVM to classify sign language isolated signs. The process involves two layer classifications. At first, coarse classification is done according to single classifier and second classification is fusion based on combination methods. Experimental results demonstrate that the classifier fusion approach can be used reliably in classifying some signs of native Indian sign language.