A Deep Learning Framework for Real-Time Sign Language Recognition Based on Transfer Learning

A Deep Learning Framework for Real-Time Sign Language Recognition Based on Transfer Learning

© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Vijeeta Patil, Sujatha C, Shridhar Allagi, Balachandra Chikkoppa
DOI : 10.14445/22315381/IJETT-V70I6P204

How to Cite?

Vijeeta Patil, C. Sujatha, Shridhar Allagi, Balachandra Chikkoppa, "A Deep Learning Framework for Real-Time Sign Language Recognition Based on Transfer Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 32-41, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P204

Hearing Impairment is common among all the groups irrespective of gender, location, and genes. The cause of it may vary according to the geographical and biological aspects. However, for the betterment of humankind, the solution to this is obvious, either through medical or with the help of technology. Sign Language recognition is a worldwide concern across the globe. The use of technology has a scope in aiding the necessary help in recognition of sign language. The major challenge lies in detecting and understanding signs, as the language differs across the various geographical regions, and there are no specific rules for understanding them. Hence, this research article uses a transfer learning algorithm with TensorFlow object detection to recognize the sign language. The proposed model has achieved an accuracy of around 97.87% for different types of sentences used in the experimentation. The main advantage of the proposed model is that it is feasible to use different sign languages, such as American sign language and any regional sign language. The system is helpful to the deaf and dumb community's society and encourages such people's upliftment.

Machine Learning, Transfer Learning, Sign Language, Object Recognition, and American Sign Language.

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