Design and Development of Teaching and Learning Tool Using Sign Language Translator to Enhance the Learning Skills for Students With Hearing and Verbal Impairment

This research paper presents a system designed for the students with verbal and hearing impairments by enabling realtime Sign-to-Text and Text-to-Sign Language conversion, with a specific focus on the Indian Sign Language (ISL). The proposed study aligns to the United Nations Sustainable Development Goal (SDG) of Quality Education. The system leverages cutting-edge technologies, MediaPipe for holistic key point extraction encompassing hand and facial movements, and Long Short-Term Memory (LSTM) architecture powered by TensorFlow and Keras for accurate sign language interpretation. This comprehensive approach ensures nuanced aspects of sign language, such as facial expressions and hand movements, are faithfully represented. On the receiving end, the system excels at Text-to-Sign Language conversion, allowing non-sign language users to interact naturally with sign language users through textual input transformed into sign language animations and Sign-to-Text conversion where the information from the sign language users is converted to text which ensures smooth communication. A user-friendly web application, developed using HTML, CSS, and JavaScript, enhances accessibility and intuitive usage for realtime communication. This research represents a significant advancement in assistive technology, promoting inclusivity and communication accessibility. It underlines the transformative potential of innovation infostering a more connected and inclusive world for all, regardless of their hearing abilities