# Combining Efficient and Precise Sign Language Recognition: Good pose estimation library is all you need

arXiv: https://arxiv.org/abs/2210.00893 · HTML: https://mlsanity.com/paper/2210.00893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [Multi-channel Transformers for Multi-articulatory Sign Language Translation](https://mlsanity.com/paper/2009.00299/) (2020)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [Pose-based Sign Language Recognition using GCN and BERT](https://mlsanity.com/paper/2012.00781/) (2020)
- [Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition](https://mlsanity.com/paper/1901.11164/) (2019)

## Built on by (most cited first, 1 of 1)

- [Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network](https://mlsanity.com/paper/2509.08661/) (2025)

## Abstract (arXiv)

Sign language recognition could significantly improve the user experience for d/Deaf people with the general consumer technology, such as IoT devices or videoconferencing. However, current sign language recognition architectures are usually computationally heavy and require robust GPU-equipped hardware to run in real-time. Some models aim for lower-end devices (such as smartphones) by minimizing their size and complexity, which leads to worse accuracy. This highly scrutinizes accurate in-the-wild applications. We build upon the SPOTER architecture, which belongs to the latter group of light methods, as it came close to the performance of large models employed for this task. By substituting its original third-party pose estimation module with the MediaPipe library, we achieve an overall state-of-the-art result on the WLASL100 dataset. Significantly, our method beats previous larger architectures while still being twice as computationally efficient and almost 11 times faster on inference when compared to a relevant benchmark. To demonstrate our method's combined efficiency and precision, we built an online demo that enables users to translate sign lemmas of American sign language in their browsers. This is the first publicly available online application demonstrating this task to the best of our knowledge.