Real Time Sign Language Recognition

Abstract

Speaking with someone who have hearing loss may be quite challenging. Systems that can recognize different signs and alert regular people are thus required. Recognition of sign language is a big development in assisting deaf-mute persons. With the exception of J and Z, which require motion detection for recognition, the objective of this study is to create a model based on neural networks for precise and user-friendly sign language identification that can identify finger spelling-based hand gestures representing the ASL alphabets.