# Real-Time Sign Language Detection using Human Pose Estimation

arXiv: https://arxiv.org/abs/2008.04637 · HTML: https://mlsanity.com/paper/2008.04637/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [Sign Language Production: A Review](https://mlsanity.com/paper/2103.15910/) (2021)
- [Aligning Subtitles in Sign Language Videos](https://mlsanity.com/paper/2105.02877/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting](https://mlsanity.com/paper/2210.05404/) (2022)
- [Linguistically Motivated Sign Language Segmentation](https://mlsanity.com/paper/2310.13960/) (2023)
- [Sign Segmentation with Changepoint-Modulated Pseudo-Labelling](https://mlsanity.com/paper/2104.13817/) (2021)
- [HOD: A Benchmark Dataset for Harmful Object Detection](https://mlsanity.com/paper/2310.05192/) (2023)
- [Heatmap Distribution Matching for Human Pose Estimation](https://mlsanity.com/paper/2210.00740/) (2022)
- [Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization](https://mlsanity.com/paper/2306.08861/) (2023)
- [Fingerspelling Detection in American Sign Language](https://mlsanity.com/paper/2104.01291/) (2021)
- [WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language](https://mlsanity.com/paper/2203.06096/) (2022)
- [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024)
- [Deep Understanding of Sign Language for Sign to Subtitle Alignment](https://mlsanity.com/paper/2503.03287/) (2025)
- [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025)
- [Phonology Recognition in American Sign Language](https://mlsanity.com/paper/2110.00453/) (2021)
- [WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users](https://mlsanity.com/paper/2404.17063/) (2024)
- [SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space](https://mlsanity.com/paper/2504.16315/) (2025)
- [On the Importance of Signer Overlap for Sign Language Detection](https://mlsanity.com/paper/2303.10782/) (2023)
- [ISLTranslate: Dataset for Translating Indian Sign Language](https://mlsanity.com/paper/2307.05440/) (2023)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation](https://mlsanity.com/paper/2511.00270/) (2025)
- [Aikyam: A Video Conferencing Utility for Deaf and Dumb](https://mlsanity.com/paper/2312.05962/) (2023)
- [An Open-Source American Sign Language Fingerspell Recognition and Semantic Pose Retrieval Interface](https://mlsanity.com/paper/2408.09311/) (2024)
- [iLSU-T: an Open Dataset for Uruguayan Sign Language Translation](https://mlsanity.com/paper/2507.21104/) (2025)
- [Evaluation of Pose Estimation Systems for Sign Language Translation](https://mlsanity.com/paper/2604.24609/) (2026)
- [Toward Signing Activity Projection in Sign Language Interaction](https://mlsanity.com/paper/2606.09424/) (2026)
- [A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition](https://mlsanity.com/paper/2610.14680/) (2026)

## Abstract (arXiv)

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 estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%, evaluated on the DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.