  The objective of sign language recognition is to bridge the communication gap
between the deaf and the hearing. Numerous previous works train their models
using the well-established connectionist temporal classification (CTC) loss.
During the inference stage, the CTC-based models typically take the entire sign
video as input to make predictions. This type of inference scheme is referred
to as offline recognition. In contrast, while mature speech recognition systems
can efficiently recognize spoken words on the fly, sign language recognition
still falls short due to the lack of practical online solutions. In this work,
we take the first step towards filling this gap. Our approach comprises three
phases: 1) developing a sign language dictionary encompassing all glosses
present in a target sign language dataset; 2) training an isolated sign
language recognition model on augmented signs using both conventional
classification loss and our novel saliency loss; 3) employing a sliding window
approach on the input sign sequence and feeding each sign clip to the
well-optimized model for online recognition. Furthermore, our online
recognition model can be extended to boost the performance of any offline
model, and to support online translation by appending a gloss-to-text network
onto the recognition model. By integrating our online framework with the
previously best-performing offline model, TwoStream-SLR, we achieve new
state-of-the-art performance on three benchmarks: Phoenix-2014, Phoenix-2014T,
and CSL-Daily. Code and models will be available at
https://github.com/FangyunWei/SLRT
