Vision-Based American Sign Language Classification Approach via Deep Learning

TL;DR

This paper proposes a deep learning model to classify American Sign Language letters, aiming to improve communication for hearing-impaired individuals by addressing language barriers.

Contribution

A novel deep learning approach specifically designed for classifying ASL letters to facilitate communication for the hearing-impaired.

Findings

Achieved accurate classification of ASL letters

Demonstrated the effectiveness of deep learning in sign language recognition

Provided a foundation for future ASL translation systems

Abstract

Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.