  We propose a lightweight real-time sign language detection model, as we
identify the need for such a case in videoconferencing. We extract optical flow
features based on human pose estimation and, using a linear classifier, show
these features are meaningful with an accuracy of 80%, evaluated on the DGS
Corpus. Using a recurrent model directly on the input, we see improvements of
up to 91% accuracy, while still working under 4ms. We describe a demo
application to sign language detection in the browser in order to demonstrate
its usage possibility in videoconferencing applications.
