Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
This work addresses the understudied area of sign language interpretation, offering a solution for improving communication accessibility, though it appears incremental as it applies existing deep learning techniques to ASL data.
The paper tackles the problem of recognizing gestures in American Sign Language (ASL) by using deep convolutional networks to classify images of letters and digits, achieving results that demonstrate the method's effectiveness in this domain.
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.