Real Time Indian Sign Language Recognition using Convolutional Neural Network

Registration ID: IJNRD_213474

Published ID: IJNRD2402076

DOI: Click Here to Get

Manpreet Sidhu , Snehal Hon , Sandesh Marathe , Tushar A. Rane

Convolutional Neural Network (CNN), Computer Vision, MediaPipe Holistic, Recurrent Neural Networks, SLR (Sign Language Recognition), SLT (Sign Language Translation).

Through sign language recognition software in particular, technology has made great strides in recent years in bridging the communication gap for those with hearing and speech impairments. To interpret sign language gestures, these systems make use of cutting-edge methodologies like deep learning, computer vision, and neural networks. For instance, a study on Indian Sign Language employed real-time processing and grid-based characteristics to recognize different hand movements without the usage of external hardware. Another method for identifying and categorizing American Sign Language motions made use of the HSV color scheme and Convolutional Neural Networks (CNN). These developments mark significant steps forward in advancing hearing-impaired people’s communication, encouraging social integration, and cultivating a deeper comprehension of sign language in a variety of circumstances.

Published Paper   E-Certificate

Manpreet Sidhu, Snehal Hon, Sandesh Marathe, & Tushar A. Rane (February-2024). Real Time Indian Sign Language Recognition using Convolutional Neural Network. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(2), a683-a689. https://ijnrd.org/papers/IJNRD2402076.pdf

Volume 9 Issue 2, February-2024

Pages : a683-a689

Paper Reg. ID: IJNRD_213474

Published Paper Id: IJNRD2402076

Downloads: 000122015

Research Area: Information Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402076.pdf

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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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