Authors :
Ms. Livya George; Nimisha Elangikkal; Nina Joseph, Ronica Ross; Sharon Joy
Volume/Issue :
Volume 6 - 2021, Issue 6 - June
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3jez1RC
Abstract :
People having hearing and speaking disabilities
will have problems communicating with other people.
This creates a gap between them. To avoid this problem,
they use some special gestures to express their thoughts.
These gestures have different meanings. They are defined
as “Sign Language”. Sign language is very important for
deaf and mute people because they are the primary means
of communication between both normal people and
between themselves. It is most commonly used for people
with talking and hearing disorders to communicate. In
this application, we present a simple structure for sign
language recognition. Our system involves implementing
such an application that detects predefined signs through
hand gestures. For the detection of gestures, we use a
basic level of hardware components like a camera, and
interfacing is needed. Our system would be a
comprehensive User-friendly Based application built on
Convolutional Neural Networks. The hand gestures are
recognized by three main steps. First, the dataset is
created by capturing images and these images are
preprocessed by resizing, masking, and converting RGB
into grayscale images. Secondly, after creating the dataset,
we have to train the system using the Convolutional
Neural Network, and using the trained classifier model
the given sign is recognized. Thus, the recognized sign is
displayed. We expect that the overall method of this
application may attract technophiles with an extensive
introduction in the sector of automated gesture and sign
language recognition, and may help in future works in
these fields.
Keywords :
Convolutional Neural Network ; Preprocessing ; Sign Language ; ReLU
People having hearing and speaking disabilities
will have problems communicating with other people.
This creates a gap between them. To avoid this problem,
they use some special gestures to express their thoughts.
These gestures have different meanings. They are defined
as “Sign Language”. Sign language is very important for
deaf and mute people because they are the primary means
of communication between both normal people and
between themselves. It is most commonly used for people
with talking and hearing disorders to communicate. In
this application, we present a simple structure for sign
language recognition. Our system involves implementing
such an application that detects predefined signs through
hand gestures. For the detection of gestures, we use a
basic level of hardware components like a camera, and
interfacing is needed. Our system would be a
comprehensive User-friendly Based application built on
Convolutional Neural Networks. The hand gestures are
recognized by three main steps. First, the dataset is
created by capturing images and these images are
preprocessed by resizing, masking, and converting RGB
into grayscale images. Secondly, after creating the dataset,
we have to train the system using the Convolutional
Neural Network, and using the trained classifier model
the given sign is recognized. Thus, the recognized sign is
displayed. We expect that the overall method of this
application may attract technophiles with an extensive
introduction in the sector of automated gesture and sign
language recognition, and may help in future works in
these fields.
Keywords :
Convolutional Neural Network ; Preprocessing ; Sign Language ; ReLU