In our interviews with people who work with speech impaired persons, we learned that 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, we created 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, we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98% test accuracy on our dataset, and we compared it with popular CNN models.
[1]
Radha Poovendran,et al.
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
,
2017,
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).
[2]
Ting Liu,et al.
Recent advances in convolutional neural networks
,
2015,
Pattern Recognit..
[3]
Yoshua Bengio,et al.
Gradient-based learning applied to document recognition
,
1998,
Proc. IEEE.
[4]
Matthew D. Zeiler.
ADADELTA: An Adaptive Learning Rate Method
,
2012,
ArXiv.
[5]
R. Mitchell,et al.
How Many People Use ASL in the United States? Why Estimates Need Updating
,
2006
.