Comprehensive Deep Learning System for Sign Language Recognition, Translation and Video Generation

Authors: Dr. Rupali S. Khule, Mr. Saad Ansari, Mr. Pranav Bhujade, Mr. Omkar Gupta, Mr. Rutvik Patil

Country: India

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Abstract: This paper presents a novel real-time sign language detection system designed to enhance communication between the deaf and hard-of-hearing community and non-signers. Utilizing standard web cameras, the system captures and analyses hand and facial gestures, employing advanced computer vision and deep learning techniques to recognize sign language gestures. Key markers corresponding to specific signs are identified and translated into voice output and on-screen text, providing a dual-output feature that fosters inclusivity and accessibility. By enabling real-time interpretation through voice and visual representation, this technology bridges communication gaps, making interactions more seamless for sign language users and those unfamiliar with it. The proposed system is adaptable for integration into webcams and other camera-equipped devices, offering potential applications across various sectors, including education and healthcare, ultimately improving understanding and interaction for sign language users.

Keywords: Sign Language, Webcam, Real-Time Detection, Computer Vision, Deep Learning

Paper Id: 232486

Published On: 2025-05-14

Published In: Volume 13, Issue 3, May-June 2025

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