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.

翻译：本文提出了一种新颖的基于关键点的分类模型，旨在识别连续手语序列中的英国手语（BSL）词汇。我们使用BOBSL数据集评估了模型的性能，结果表明基于关键点的方法在计算效率和内存使用方面均优于基于RGB的模型。此外，该方法训练速度更快，且所需计算资源更少。据我们所知，这是首次将基于关键点的模型应用于BSL词汇分类，因此无法与现有工作进行直接比较。