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.

该研究利用 SPOTER 架构和 MediaPipe 技术，实现更高效、精准的手语识别，成功创建了首个公开的在线手语翻译应用。