Using Deep Convolutional Networks for Gesture Recognition in American Sign Language

TL;DR

This paper explores the application of deep convolutional neural networks to classify images of American Sign Language gestures, focusing on letters and digits, to improve sign language recognition systems.

Contribution

It introduces a novel approach using deep convolutional networks specifically for classifying ASL gestures, advancing sign language interpretation technology.

Findings

Achieved high accuracy in classifying ASL letters and digits

Demonstrated the effectiveness of deep learning for gesture recognition

Provided a foundation for real-time sign language translation systems

Abstract

In the realm of multimodal communication, sign language is, and continues to be, one of the most understudied areas. In line with recent advances in the field of deep learning, there are far reaching implications and applications that neural networks can have for sign language interpretation. In this paper, we present a method for using deep convolutional networks to classify images of both the the letters and digits in American Sign Language.