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This work presents a meta study covering around 300 published sign languagerecognition papers with over 400 experimental results. It includes most papersbetween the start of the field in 1983 and 2020. Additionally, it covers afine-grained analysis on over 25 studies that have compared their recognitionapproaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of thefield. Research in the domain of sign language recognition has progressedsignificantly in the last decade, reaching a point where the task attracts muchmore attention than ever before. This study compiles the state of the art in aconcise way to help advance the field and reveal open questions. Moreover, allof this meta study's source data is made public, easing future work with it andfurther expansion. The analyzed papers have been manually labeled with a set ofcategories. The data reveals many insights, such as, among others, shifts inthe field from intrusive to non-intrusive capturing, from local to globalfeatures and the lack of non-manual parameters included in medium and largervocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with avocabulary of 1080 signs represents the only resource for large vocabularycontinuous sign language recognition benchmarking world wide.

Quantitative Survey of the State of the Art in Sign Language Recognition