A Sign Language Recognition Approach for Human-Robot Symbiosis

Abstract

This paper introduces a new concept for the establishment of human-robot symbiotic relationship.The system is based on the implementation of knowledge-based image processing methodologies for model based vision and intelligent task scheduling for an autonomous social robot.This paper aims to develop an automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using neural network with backpropagation algorithm.System deals with images of bare hands to achieve the recognition task.For each individual sign 10 sample images have been considered, which means in total300 samples have been processed.In order to compare between the training set of signs and the considered sample images, are converted into feature vectors.Experimental results reveal that this can recognize selected ASL signs (accuracy of 92.00%).Finally, the system has been implemented issuing hand gesture commands for ASL to a robot car, named "Moto-robo".