Real-Time Sign Language Recognition using Hand Tracking and Deep Learning

The development of a real-time sign languagedetectionsystem is crucial for fostering effectivecommunication and inclusivity for individuals with hearingimpairments. By providing a means to interpret andunderstand sign language gestures in real-time, this projectaddresses a fundamental barrier faced by the deaf andhard-of-hearing community. In this research, we present acomprehensive investigation of our real-time sign languagedetection system, which combines hand tracking andclassification techniques. The results demonstrate highconfidence scores and real-time responsiveness, making it apromising solution for facilitating communication betweenindividuals with hearing impairments and non-sign languageusers. The proposed system holds significant potential forapplications in educational, social, and professional settings,ultimately contributing to a more inclusive and accessiblesociety