Published Paper Details:

Communication between the Speech-impaired and normal people has always been difficult. It is a challenge to develop an appropriate technology to help these people communicate with others. Recognizing the sign language of deaf and dumb people is the only way that offers speech-impaired a proper platform to easily communicate with the normal people. According to Census 2011, out of 6.79 crore people 21,932 people are speech-impaired in Tamil Nadu who speak Tamil Language. It is a tedious process to make all the normal people learn the sign language, but whereas it is an optimal process for a machine to do this job. There are numerous applications developed for recognizing American Sign Language (ASL), but few efficient applications for recognizing Dravidian Sign Language are developed. So, the goal is to develop a gesture recognition system for Dravidian Sign Language (Tamil) that efficiently translates the sign language into an equivalent Tamil text. This project bridges the communication gap between the speech-impaired and normal people with the help of Human-Computer Interaction (HCI) by using the CNN (Convolutional Neural Networks) technique.