• Mavushiga Shree JD
• Nathanya Siphora C
• Pooja A

This research focuses on developing a real-time sign language detection system using Convolutional Neural Networks (CNNs). The system aims to bridge the communication gap between sign language users and non-sign language users by accurately recognizing and interpreting sign language gestures. By leveraging diverse and annotated datasets, the CNN model is trained and optimized for robust performance. Through rigorous evaluation, the system demonstrates accuracy, robustness, and real-time capability. The developed system holds significant potential for enhancing accessibility and inclusivity, benefiting domains such as assistive technologies, human-computer interaction, and education. It enables efficient communication and fosters inclusivity for individuals who are deaf or hard of hearing. This research contributes to breaking down communication barriers, facilitating seamless communication between sign language users and others.

Gesture recognition, python, OpenCV, machine learning, sign language detection

"GestureNet : A DEEP LEARNING APPROACH FOR REAL-TIME SIGN LANGUAGE REGOCNITION AND TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.d118-d127, July-2023, Available :http://www.jetir.org/papers/JETIR2307315.pdf

"GestureNet : A DEEP LEARNING APPROACH FOR REAL-TIME SIGN LANGUAGE REGOCNITION AND TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppd118-d127, July-2023, Available at : http://www.jetir.org/papers/JETIR2307315.pdf