Authors :
Ayushi N. Patani; Varun S. Gawande; Jash V. Gujarathi; Vedant K. Puranik; Tushar A .Rane
Volume/Issue :
Volume 6 - 2021, Issue 4 - April
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/33tmwsc
Abstract :
Interpreting deaf-mute people has always
been a problem for people as they primarily rely on sign
language for communicating. Active participation of the
deaf-mute community still remains at an elementary
stage, despite multiple nations providing resources for
the same, like a sign language interpreter and
communicator of news in the country of New Zealand.
Perturbing situations such as kidnapping, deception, fire
breakout or any other situations of general agony could
further exacerbate this barrier of communication, as the
mute people try their best to communicate, but the
majority remains oblivious to their language. Therefore,
bridging the gap between these two worlds is of utmost
necessity. This paper aims to briefly acquaint the reader
with how sign language communication works and puts
forward research conducted in this field that explains
how to capture and recognize sign language and also
attempts to suggest a systemized solution
Keywords :
Hilbert Curve, Support Vector Machines, Random Forests, Artificial Neural Network, Feed-forward Backpropagation, Hough Transform, Convolutional Neural Networks, Stacked Deionized Decoders, Multilayer Perceptron Neural Network, Adaline Neural Networ
Interpreting deaf-mute people has always
been a problem for people as they primarily rely on sign
language for communicating. Active participation of the
deaf-mute community still remains at an elementary
stage, despite multiple nations providing resources for
the same, like a sign language interpreter and
communicator of news in the country of New Zealand.
Perturbing situations such as kidnapping, deception, fire
breakout or any other situations of general agony could
further exacerbate this barrier of communication, as the
mute people try their best to communicate, but the
majority remains oblivious to their language. Therefore,
bridging the gap between these two worlds is of utmost
necessity. This paper aims to briefly acquaint the reader
with how sign language communication works and puts
forward research conducted in this field that explains
how to capture and recognize sign language and also
attempts to suggest a systemized solution
Keywords :
Hilbert Curve, Support Vector Machines, Random Forests, Artificial Neural Network, Feed-forward Backpropagation, Hough Transform, Convolutional Neural Networks, Stacked Deionized Decoders, Multilayer Perceptron Neural Network, Adaline Neural Networ
