• Yash Kanwar
• Param Jethwa
• Rahul Thilak
• Siddharth Aglesar
• Umesh Mohite

Sign language plays a vital role in facilitating communication for individuals with hearing disabilities. However, the lack of automated tools for sign language interpretation presents a significant barrier to effective communication with the wider community. To address this challenge, we propose "Sign Language Detection," a system combining real-time hand gesture recognition and deep learning classification. It encompasses data collection, model training, and real-time gesture recognition interfaces. Users capture and label hand gestures via webcam, which are preprocessed images, and used to train a convolutional neural network (CNN) for accurate classification. The deployed model interprets gestures in real-time, facilitating effective communication via user-friendly interfaces. Our system enhances inclusivity and accessibility in digital communication, bridging linguistic divides, empowering individuals with hearing disabilities and fostering inclusive communication.

Sign language detection, hand gesture recognition, real-time interpretation, deep learning classification, accessibility, inclusivity, hearing disabilities, communication barriers, digital communication, user-friendly interfaces, assistive technology, machine learning, computer vision, gesture recognition system.

"Sign Language Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d1-d7, April-2024, Available :http://www.jetir.org/papers/JETIR2404301.pdf

"Sign Language Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd1-d7, April-2024, Available at : http://www.jetir.org/papers/JETIR2404301.pdf