Sign Language Recognition with Visual Attention

EasyChair Preprint no. 2312

Abstract

Sign Lnaguage Recognition hold significant importance to move towards a globally connected generation, by laying the foundation in the development of support systems for the deaf community.
Several CNN based approaches have been explored in the past to tackle the recognition of hand sign gestures. In this work, we implement the techniques that utilize the phenomenon of spatial attention for the classification and recognition of the American Sign Language (ASL) in natural scenario. 
We experiment on the ASL Alphabet dataset, which is a publicly available dataset, to analyze the performance of the proposed framework.

Keyphrases: ASL, CNN, Faster RCNN, Sign Language Recognition, visual attention

@Booklet{EasyChair:2312,
  author = {Shweta Upadhyay and R. K. Sharma and Prashant Singh Rana},
  title = {Sign Language Recognition with Visual Attention},
  howpublished = {EasyChair Preprint no. 2312},

  year = {EasyChair, 2020}}