Sign Language Converter Using Feature Extractor and PoseNet

Human beings commune with each other to convey thoughts, expressions, experiences and so on to the people around them. But this case is not the same when it comes to the deaf and mute people. Sign language paves the way for people with such difficulties to communicate with one another. The aim of th...

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Bibliographic Details
Published in:Webology 2022-01, Vol.19 (1), p.5476-5486
Main Authors: M.N., Pushpalatha, A., Parkavi, R.S., Sachin, Vadakkan, Amy S, Khateeb, Aakifha, K.P, Deeksha
Format: Article
Language:English
Subjects:
Algorithms
Classification
Communication
Deafness
Discriminant analysis
Literature reviews
Neural networks
Sensors
Sign language
Voice recognition
Webcams
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Summary:Human beings commune with each other to convey thoughts, expressions, experiences and so on to the people around them. But this case is not the same when it comes to the deaf and mute people. Sign language paves the way for people with such difficulties to communicate with one another. The aim of this paper is to facilitate easy and accurate communication between people who have hearing and speaking disabilities, and those who do not. This paper shows how the communication gap can be bridged using sign language to text and audio converter with the help of Feature Extractor and Posenet with an accuracy of 92%. A webcam is used to capture the sign language shown by a person. Posenet with Artificial Neural Network is used to classify essential words used in day to day life. Various parts of the body are tracked by the webcam and then converted to text and audio to convey what the person is trying to express in real time.
ISSN:1735-188X
1735-188X
DOI:10.14704/WEB/V19I1/WEB19368