Sign Language Recognition: A Large-scale Multi-view Dataset and Comprehensive Evaluation

Vision-based sign language recognition is an extensively researched problem aimed at advancing communication be-tween deaf and hearing individuals. Numerous Sign Lan-guage Recognition (SLR) datasets have been introduced to promote research in this field, spanning multiple languages, vocabulary sizes, and signers. However, most existing pop-ular datasets focus predominantly on the frontal view of signers, neglecting visual information from other perspec-tives. In practice, many sign languages contain words that have similar hand movements and expressions, making it challenging to differentiate between them from a single frontal view. Although a few studies have proposed sign language datasets using multi-view data, these datasets remain limited in vocabulary size and scale, hindering their gener-alizability and practicality. To address this issue, we in-troduce a new large-scale, multi-view sign language recog-nition dataset spanning 1,000 glosses and 30 signers, re-sulting in over 84,000 multi-view videos. To the best of our knowledge, this is the first multi-view sign language recognition dataset of this scale. In conjunction with of-fering a comprehensive dataset, we perform extensive ex-periments to assess the performance of state-of-the-art Sign Language Recognition models utilizing on our dataset. The findings indicate that utilizing multi-view data substantially enhances model accuracy across all models, with a maxi-mum performance improvement of up to 19.75% compared to models trained on single-view data. Our dataset and baseline models are publicly accessible on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Available at https://github.com/Etdihatthoc/Multi-VSL_WACV_2025.