Sign Language Recognition by Hand Gesture for Deaf and Speech Impaired Community using ML

Rakesh K. Kadu, Asish V. Chandak, Purshottam J. Assudani, Aditi Tiwari, Nirja Jaurkar

Abstract: In a world where communication is predominantly verbal, the deaf and speech-impaired community face unique challenges in expressing themselves and understanding others. These individuals often rely on non-verbal forms of communication, such as sign language and hand gestures, to convey their thoughts and emotions. Recognizing the importance of inclusivity and accessibility, technological advancements have paved the way for innovative solutions that aim to bridge the communication gap for the deaf and speech-impaired population. Sign language, a rich and expressive visual language, serves as a primary means of communication for many in the deaf community. It involves the use of manual gestures, facial expressions, and body movements to convey complex linguistic information. As technology continues to evolve, the integration of sign language recognition systems has become a focal point in developing tools that enhance communication and foster inclusivity. This paper aimed at developing a Sign Language and Hand Gesture Recognition system. The core objective of this paper is to design an accessible, affordable platform that utilizes advanced machine learning and computer vision techniques to recognize and interpret sign language and hand gestures. The system enables real-time translation of sign language into text. In conclusion, the Sign Language and Hand Gesture Recognition system described in this paper presents a significant advancement in assistive technology, promoting inclusivity and equality for the Deaf and Speech Impaired community.