  This work presents a meta study covering around 300 published sign language
recognition papers with over 400 experimental results. It includes most papers
between the start of the field in 1983 and 2020. Additionally, it covers a
fine-grained analysis on over 25 studies that have compared their recognition
approaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of the
field. Research in the domain of sign language recognition has progressed
significantly in the last decade, reaching a point where the task attracts much
more attention than ever before. This study compiles the state of the art in a
concise way to help advance the field and reveal open questions. Moreover, all
of this meta study's source data is made public, easing future work with it and
further expansion. The analyzed papers have been manually labeled with a set of
categories. The data reveals many insights, such as, among others, shifts in
the field from intrusive to non-intrusive capturing, from local to global
features and the lack of non-manual parameters included in medium and larger
vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a
vocabulary of 1080 signs represents the only resource for large vocabulary
continuous sign language recognition benchmarking world wide.
