Sign Language Recognition using Deep Learning

The substantial communication barriers faced by deaf or hard-of-hearing individuals who primarily use sign language create challenges for those who do not use it. Given that sign language can be difficult for non-users to understand, there is a pressing need for systems that bridge this gap. A real-time sign language recognition system is proposed, utilizing deep learning techniques, to accurately interpret various signs and convert them into written language. By employing a custom dataset and the MediaPipe framework for hand landmarks detection, the system processes input data through comprehensive preprocessing and training phases. The goal is to create an intuitive and efficient solution that enhances communication accessibility, promotes inclusivity, and empowers the hearing-impaired community to engage more fully with society.