  Sign language recognition is a challenging and often underestimated problem
comprising multi-modal articulators (handshape, orientation, movement, upper
body and face) that integrate asynchronously on multiple streams. Learning
powerful statistical models in such a scenario requires much data, particularly
to apply recent advances of the field. However, labeled data is a scarce
resource for sign language due to the enormous cost of transcribing these
unwritten languages.
  We propose the first real-life large-scale sign language data set comprising
over 25,000 annotated videos, which we thoroughly evaluate with
state-of-the-art methods from sign and related action recognition. Unlike the
current state-of-the-art, the data set allows to investigate the generalization
to unseen individuals (signer-independent test) in a realistic setting with
over 200 signers. Previous work mostly deals with limited vocabulary tasks,
while here, we cover a large class count of 1000 signs in challenging and
unconstrained real-life recording conditions. We further propose I3D, known
from video classifications, as a powerful and suitable architecture for sign
language recognition, outperforming the current state-of-the-art by a large
margin. The data set is publicly available to the community.
