New keypoint-based approach for recognising British Sign Language (BSL) from sequences

TL;DR

This paper introduces a novel keypoint-based model for recognizing British Sign Language words from continuous sequences, demonstrating improved efficiency and reduced resource requirements over RGB-based methods.

Contribution

It is the first application of a keypoint-based approach for BSL word classification, offering a new method that outperforms RGB-based models in efficiency.

Findings

Outperforms RGB-based models in computational efficiency

Requires less memory and training time

First application of keypoint-based BSL recognition

Abstract

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.