Vivek Bheda, Dianna Radpour
Published 2017-10-18Version 1
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.
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Searching for fingerspelled content in American Sign Language
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