TITLE: A New Dataset and Proposed Convolutional Neural Network Architecture for Classification of American Sign Language Digits 

ABSTRACT: According to interviews with people who work with speech impaired persons,
speech impaired people have difficulties in communicating with other people
around them who do not know the sign language, and this situation may cause
them to isolate themselves from society and lose their sense of independence.
With this paper, to increase the quality of life of individuals with
facilitating communication between individuals who use sign language and who do
not know this language, a new American Sign Language (ASL) digits dataset that
can help to create machine learning algorithms which need to large and varied
data to be successful created and published as Sign Language Digits Dataset on
Kaggle Datasets web page, a proposal Convolutional Neural Network (CNN)
architecture that can get 98% test accuracy on our dataset presented, and
compared with the existing popular CNN models.
According to interviews with people who work with speech impaired persons,
speech impaired people have difficulties in communicating with other people
around them who do not know the sign language, and this situation may cause
them to isolate themselves from society and lose their sense of independence.
With this paper, to increase the quality of life of individuals with
facilitating communication between individuals who use sign language and who do
not know this language, a new American Sign Language (ASL) digits dataset that
can help to create machine learning algorithms which need to large and varied
data to be successful created and published as Sign Language Digits Dataset on
Kaggle Datasets web page, a proposal Convolutional Neural Network (CNN)
architecture that can get 98% test accuracy on our dataset presented, and
compared with the existing popular CNN models.
A New Dataset and Proposed Convolutional Neural Network Architecture for Classification of American Sign Language Digits
http://arxiv.org/abs/2011.08927v2