Combining Efficient and Precise Sign Language Recognition: Good pose estimation library is all you need
This work addresses the need for efficient and accurate sign language recognition for d/Deaf people using consumer technology, representing an incremental improvement over existing light methods.
The paper tackled the problem of sign language recognition by improving the SPOTER architecture with the MediaPipe library, achieving state-of-the-art results on the WLASL100 dataset while being twice as computationally efficient and 11 times faster in inference compared to benchmarks.
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.