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

TL;DR

This comprehensive meta study reviews 300 sign language recognition papers from 1983 to 2020, analyzing trends, benchmarks, and open questions to guide future research in the field.

Contribution

It provides a detailed, categorized analysis of past research, including a public dataset, and highlights key shifts and gaps in sign language recognition methods.

Findings

Shift from intrusive to non-intrusive capturing methods

Transition from local to global features in recognition systems

Lack of non-manual parameters in larger vocabulary recognition

Abstract

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.