Authors :
Pakshal Jain; Chetan Kumarkar; Monali Bambode; Pradnya Kad; Pradnya Tapkir-Kad
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/38dfMV0
DOI :
https://doi.org/10.5281/zenodo.6477839
Abstract :
Sign language is a unique type of
communication language which is essential for bridging
the commuication gap between deaf and dumb people.
In each sign language, there are various signs with
variations in palm size, shape and motion and placement
of hand which plays a major in each sign. A large
number of applications have been put forward by
various researchers. In the past few years, in these
applications many remarkable changes have been made
using deep learning concepts. Throughout this survey,
we analysed these applications of hand gesture
recognition using deep learning concepts from the last
few years. Although there were many notable
improvements in the accuracy in hand gesture
recognition, there are still many complications that
needs to be resolved. We put forward a taxonomy to
clasify the proposed apllications for future lines of
research in the field. Our objective is to develop an
application that can recognize hand gestures and signs.
We will train that model in a way that sign language will
be converted into text and audio. This will help people
communicate with people who are deaf and blind. The
application will recognize hand gestures by comparing
the input with pre-existing datasets formed using the
American sign Language. Here the input will be in the
form of a real-time video of hand signals of sign
language. We will convert those signs into text as well as
audio as output for users to recognize the signs which
are captured by camera and presented by the sign
language speaker.
Keywords :
Hand Gestures, Sign Language, Communication, Convolutional Neural Network(CNN).
Sign language is a unique type of
communication language which is essential for bridging
the commuication gap between deaf and dumb people.
In each sign language, there are various signs with
variations in palm size, shape and motion and placement
of hand which plays a major in each sign. A large
number of applications have been put forward by
various researchers. In the past few years, in these
applications many remarkable changes have been made
using deep learning concepts. Throughout this survey,
we analysed these applications of hand gesture
recognition using deep learning concepts from the last
few years. Although there were many notable
improvements in the accuracy in hand gesture
recognition, there are still many complications that
needs to be resolved. We put forward a taxonomy to
clasify the proposed apllications for future lines of
research in the field. Our objective is to develop an
application that can recognize hand gestures and signs.
We will train that model in a way that sign language will
be converted into text and audio. This will help people
communicate with people who are deaf and blind. The
application will recognize hand gestures by comparing
the input with pre-existing datasets formed using the
American sign Language. Here the input will be in the
form of a real-time video of hand signals of sign
language. We will convert those signs into text as well as
audio as output for users to recognize the signs which
are captured by camera and presented by the sign
language speaker.
Keywords :
Hand Gestures, Sign Language, Communication, Convolutional Neural Network(CNN).
