Regional Sign Language Learning and Translation Using the Transfer Learning Model

Registration ID: IJNRD_219869

Published ID: IJNRD2405155

DOI: Click Here to Get

SWATHILAKSHMI V , SUDHAARSHAN R , SANJAY R , Gururaman T N

CNN algorithm, GRU and LSTM architectures, Hand gesture.

This study discusses the use of hand gestures in sign language for non-verbal communication, particularly among individuals who are deaf or have speech impairments. Predicting sign language gestures in Tamil and Telugu using CNN algorithms may lack accuracy due to the struggle with capturing sequential and temporal dependencies. To address this, the researchers propose a system that combines GRU and LSTM architectures, designed for modeling sequences. These recurrent neural network models excel at understanding the intricate movements of sign language, picking up on patterns over time with the help of memory mechanisms. Switching to GRU and LSTM architectures boosts accuracy and promotes inclusivity, improving communication for the deaf and speech-impaired.

Published Paper   E-Certificate

SWATHILAKSHMI V, SUDHAARSHAN R, SANJAY R, & Gururaman T N (May-2024). Regional Sign Language Learning and Translation Using the Transfer Learning Model. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), b384-b397. https://ijnrd.org/papers/IJNRD2405155.pdf

Volume 9 Issue 5, May-2024

Pages : b384-b397

Paper Reg. ID: IJNRD_219869

Published Paper Id: IJNRD2405155

Downloads: 000121981

Research Area: Engineering

Country: Puducherry, Puducherry, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405155.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405155

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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