  Today's sign language recognition models require large training corpora of
laboratory-like videos, whose collection involves an extensive workforce and
financial resources. As a result, only a handful of such systems are publicly
available, not to mention their limited localization capabilities for
less-populated sign languages. Utilizing online text-to-video dictionaries,
which inherently hold annotated data of various attributes and sign languages,
and training models in a few-shot fashion hence poses a promising path for the
democratization of this technology. In this work, we collect and open-source
the UWB-SL-Wild few-shot dataset, the first of its kind training resource
consisting of dictionary-scraped videos. This dataset represents the actual
distribution and characteristics of available online sign language data. We
select glosses that directly overlap with the already existing datasets
WLASL100 and ASLLVD and share their class mappings to allow for transfer
learning experiments. Apart from providing baseline results on a pose-based
architecture, we introduce a novel approach to training sign language
recognition models in a few-shot scenario, resulting in state-of-the-art
results on ASLLVD-Skeleton and ASLLVD-Skeleton-20 datasets with top-1 accuracy
of $30.97~\%$ and $95.45~\%$, respectively.
