New keypoint-based approach for recognising British Sign Language (BSL) from sequences
This work addresses sign language recognition for accessibility, but it is incremental as it adapts an existing keypoint approach to a new domain (BSL) without broad SOTA impact.
The paper tackles the problem of recognizing British Sign Language (BSL) words from continuous sequences by introducing a keypoint-based classification model, which outperforms RGB-based methods in computational efficiency, memory usage, and training speed on the BOBSL dataset.
In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational efficiency and memory usage. Furthermore, it offers expedited training times and demands fewer computational resources. To the best of our knowledge, this is the inaugural application of a keypoint-based model for BSL word classification, rendering direct comparisons with existing works unavailable.