Implementation of Neural Network based Hand Gesture Recognition System (International Journal of Engineering and Computer Science, 2017)
Communication is the means of exchanging information, views and expressions among different persons, in both verbal and non-verbal manner. Hand gestures are the non verbal method of communication used along with verbal communication. A more organized form of hand gesture communication is the sign language, which is  the language of communication for deaf and dumb people. Most of these physically impaired communities are dependent on sign language translators to express their thoughts to rest of the world. This causes isolation of these people in society. Hence, Sign Language Recognition (SLR) is one of the most growing fields of research today. Hand gesture is an active area of research in the computer vision, mainly for the purpose of sign language recognition and Human Computer interaction. Here, I have developed a system for hand gesture recognition of Indian sign language, in which each alphabet and numbers  of the english vocabulary  is assigned a sign which is used by deaf and the dumb to communicate with each other. An artificial neural network based classification with error backpropagation algorithm  is used for recognizing different signs and translate them into text and voice format. To implement this approach we have utilized a simple web camera to capture hand gesture images. Thus presenting a system that recognizes Indian sign language (ISL) based on hand gestures allows the user to interact with the system in natural way.