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

TL;DR

This paper enhances sign language recognition by integrating a good pose estimation library into a lightweight architecture, achieving state-of-the-art accuracy with high efficiency and real-time performance in a browser-based demo.

Contribution

It replaces the pose estimation component in the SPOTER architecture with MediaPipe, significantly improving accuracy and efficiency for in-the-wild sign language recognition.

Findings

Achieved state-of-the-art results on WLASL100 dataset.

Method is twice as computationally efficient as previous models.

Demo enables real-time sign language translation in browsers.

Abstract

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.