Real-Time Sign Language Interpretation for Inclusive Communication

Real-Time Sign Language Interpretation for Inclusive Communication

Divyesh Khairnar1, Shravan Londhe, 2, Sahil Gaikwad3, Tanmay Shewale4

1,2,3,4,, Department of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik

5Vidya Kale Lecturer of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik

6Mr.M.P.Bhandakkar Head of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik

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Abstract - Sign language plays a crucial role as a

communication tool for both the deaf and hard-of- hearing communities, enabling them to engage and interact effectively within their own community as well as with others.However, communication barriers arise when individuals unfamiliar with sign language engage with those who rely on it, underscoring the need for inclusive solutions. Real-time sign language interpretation systems, leveraging machine learning and computer vision technologies, present a promising approach to bridging this gap. These systems convert sign language gestures into spoken or written language by utilizing gesture recognition algorithms, neural networks, and natural language processing. By analyzing hand movements, facial expressions, and body language, the systems provide accurate, context-aware translations of various sign languages, such as American Sign Language (ASL), with minimal delay. This enables seamless, natural interactions, making such technologies essential for fostering inclusive communication in diverse settings.

Key Words: Sign language recognition, real- time interpretation, machine learning, com- puter vision, gesture recognition, neural networks, natural language processing, communication barriers, inclusivity, American Sign Language (ASL).