We propose a lightweight real-time sign language detection model, as we identify the need
for such a case in videoconferencing. We extract optical flow features based on human pose …

Sign language segmentation is a crucial task in sign language processing systems. It
enables downstream tasks such as sign recognition, transcription, and machine translation …

In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus.
This event marked a change of focus for the project. While before most attention had been …

Researchers in the fields of sign language and gesture studies frequently present their
participants with video stimuli showing actors performing linguistic signs or co-speech …

In this paper we survey the state of the art for the anonymisation of sign language corpora.
We begin by exploring the motivations behind anonymisation and the close connection with …

Sign language detection, identifying if someone is signing or not, is becoming crucially
important for its applications in remote conferencing software and for selecting useful sign …

This data statement of the Public DGS Corpus provides information relevant to judging the
nature of the language content of the corpus. It covers how the corpus was curated, specifies …

This project note provides an overview of which steps have been taken to make the DGS
Corpus searchable for common morphological and syntactic phenomena in DGS: negation …

Sign language recognition models require extensive training data. Effectively anonymizing
such data remains a complex endeavor due to the crucial role of facial features. While pose …

Machine learning has had a resurgence in recent years partly as a result of expanding
capabilities in computational processing with graphical processing units making …