  Recent progress in fine-grained gesture and action classification, and
machine translation, point to the possibility of automated sign language
recognition becoming a reality. A key stumbling block in making progress
towards this goal is a lack of appropriate training data, stemming from the
high complexity of sign annotation and a limited supply of qualified
annotators. In this work, we introduce a new scalable approach to data
collection for sign recognition in continuous videos. We make use of
weakly-aligned subtitles for broadcast footage together with a keyword spotting
method to automatically localise sign-instances for a vocabulary of 1,000 signs
in 1,000 hours of video. We make the following contributions: (1) We show how
to use mouthing cues from signers to obtain high-quality annotations from video
data - the result is the BSL-1K dataset, a collection of British Sign Language
(BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train
strong sign recognition models for co-articulated signs in BSL and that these
models additionally form excellent pretraining for other sign languages and
benchmarks - we exceed the state of the art on both the MSASL and WLASL
benchmarks. Finally, (3) we propose new large-scale evaluation sets for the
tasks of sign recognition and sign spotting and provide baselines which we hope
will serve to stimulate research in this area.
