# On the Importance of Signer Overlap for Sign Language Detection

arXiv: https://arxiv.org/abs/2303.10782 · HTML: https://mlsanity.com/paper/2303.10782/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Real-Time Sign Language Detection using Human Pose Estimation](https://mlsanity.com/paper/2008.04637/) (2020)

## Built on by (most cited first, 2 of 2)

- [JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing](https://mlsanity.com/paper/2311.10174/) (2023)
- [Linguistically Motivated Sign Language Segmentation](https://mlsanity.com/paper/2310.13960/) (2023)

## Abstract (arXiv)

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 data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive results that do not generalize well due to signer overlap between train and test partitions. We quantify this with a detailed analysis of the effect of signer overlap on current sign detection benchmark data sets. Comparing accuracy with and without overlap on the DGS corpus and Signing in the Wild, we observed a relative decrease in accuracy of 4.17% and 6.27%, respectively. Furthermore, we propose new data set partitions that are free of overlap and allow for more realistic performance assessment. We hope this work will contribute to improving the accuracy and generalization of sign language detection systems.