Virtual Assistant With Sign Language

Authors

  • Dr. D. S. Hirolikar   Department of Information Technology, PDEA’s College of Engineering (Manjari B.K.), Pune, India
  • Hari Shelar   Department of Information Technology, PDEA’s College of Engineering (Manjari B.K.), Pune, India
  • Prajwal Berad   Department of Information Technology, PDEA’s College of Engineering (Manjari B.K.), Pune, India
  • Mohini Gawade   Department of Information Technology, PDEA’s College of Engineering (Manjari B.K.), Pune, India
  • Yogesh Pawar   Department of Information Technology, PDEA’s College of Engineering (Manjari B.K.), Pune, India

DOI:

https://doi.org/10.32628/IJSRSET23103107

Keywords:

Deep Learning, Virtual Assistants, Tensor Flow, Convolutional Neural Network Hand Gestures, Sign Languages.

Abstract

This topic is all about developing a successful Sign language recognition and translation system which is helpful for people with certain disabilities who can’t use present voice enabled Virtual Assistants. Communication is important with people as it helps us understand them and gain knowledge. In case of Deaf & mute people, this becomes a problem. It is solved by creating a virtual assistant built to recognize hand gestures and translate them in normal language to perform tasks ordered by the user. This certainly helps other normal people to successfully communicate with deaf & mute people Without worrying about any misunderstanding at the listener’s end. There are more than 300 sign languages used by various cultural groups worldwide. In this article, we provide a technique for generating a sign language dataset using a camera, followed by the training of a TensorFlow model using transfer learning to produce a real-time sign language recognition system. Despite the small amount of the dataset, the system still performs well.

References

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Published

2023-06-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. D. S. Hirolikar, Hari Shelar, Prajwal Berad, Mohini Gawade, Yogesh Pawar "Virtual Assistant With Sign Language" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 3, pp.297-301, May-June-2023. Available at doi : https://doi.org/10.32628/IJSRSET23103107