Sign language recognition using convolutional neural network

Abstract

: Sign language detection plays a vital role in enhancing accessibility, communication, and inclusion for individuals who are deaf or hard of hearing. It is an essential tool that facilitates smooth communication in various environments, such as schools, workplaces, healthcare settings, and daily interactions. By accurately identifying and interpreting sign language gestures, this technology helps bridge communication barriers between sign language users and those unfamiliar with it, ensuring equal access to information and services. In education, this technology supports language learning, literacy, and academic achievement for deaf students. In healthcare, reliable sign language detection ensures that medical information and consultations are conveyed clearly, improving care quality for deaf patients. In the workplace, it promotes inclusivity by enabling effective communication between deaf employees, their colleagues, and clients, fostering equal opportunities and professional growth. Additionally, during emergencies, this technology can save lives by facilitating swift and precise communication between emergency responders and deaf individuals. When incorporated into assistive technologies, sign language detection enables deaf individuals to independently handle everyday tasks, such as accessing digital content, using smart devices, and engaging in online interactions.