Design and Development of Real-Time Sign Language Detection for Deaf People

Registration ID: IJNRD_190790

Published ID: IJNRD2304599

DOI: Click Here to Get

Shwetark Swanand Deshpande , Preshit Balu Ghode , Ajinkya Shivaji Gunjkar , Omkar Pratap Gadge

Sign language, Deep-learning, TensorFlow, Open-CV, Convolution Neural Network (CNN)

There is a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. With this work, we intend to take a basic step in bridging this communication gap using Sign Language Recognition. In this project Deep-Learning approach was used for model training to recognize the signs used in real time. By using TensorFlow and Open-CV we can do the detection in real time. We make use of Convolution Neural Network (CNN) for training and to classify the images of the 5 gestures for the phrases used in American sign language and the average detection rate in real-time is above 90 Percent.

Published Paper   E-Certificate

Shwetark Swanand Deshpande, Preshit Balu Ghode, Ajinkya Shivaji Gunjkar, & Omkar Pratap Gadge (April-2023). Design and Development of Real-Time Sign Language Detection for Deaf People. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), f197-f200. https://ijnrd.org/papers/IJNRD2304599.pdf

Volume 8 Issue 4, April-2023

Pages : f197-f200

Paper Reg. ID: IJNRD_190790

Published Paper Id: IJNRD2304599

Downloads: 000121990

Research Area: Computer Science & Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304599.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304599

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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