Abstract

Gesture recognition plays an important role in communication through sign language. It is a fast growing domain within computer vision and has attracted significant research due to its widespread social impact. To tackle the difficulties faced by hearing impairment, it is the need of the hour to develop a system which translates the sign language into text which can easily be recognized by the impaired people. In this paper, a static hand gesture recognition system is developed for American Sign Language using deep Convolutional Neural Network. The system architecture is light weight to make the system easily deployable and mobile. In order to achieve high accuracy on live scenarios we employ, a number of image processing techniques which assist in appropriate background subtraction and frame segmentation. Our approach focuses on mobility, cost-free and easy deployment in low computational environment. Our system achieved a testing accuracy of 96%.