A New 27 Class Sign Language Dataset Collected from 173 Individuals

After the interviews, it has been comprehended that speech-impaired individuals who use sign languages have difficulty communicating with other people who do not know sign language. Due to the communication problems, the sense of independence of speech-impaired individuals could be damaged and lead them to socialize less with society. To contribute to the development of technologies, that can reduce the communication problems of speech-impaired persons, a new dataset was presented with this paper. The dataset was created by processing American Sign Language-based photographs collected from 173 volunteers, published as 27 Class Sign Language Dataset on the Kaggle Datasets web page.

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

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.