Self-supervised learning with a contrastive VideoMoCo framework for Saudi Arabic sign language recognition using 3D convolutional networks.

ABSTRACT: Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum Contrast (VideoMoCo) paradigm integrated with a 3D ResNet-50 backbone, designed to jointly capture spatial and temporal gesture dependencies. The proposed model is pretrained on 18,000 unlabeled gesture videos and subsequently fine-tuned on the KARSL-502 dataset containing 15,400 labeled samples covering 502 distinct classes. Experimental evaluation shows that the model attains an F1-score of 92.7%, outperforming CNN-LSTM (86.0%) and Two-Stream CNN (84.5%) baselines-an improvement of nearly 9% points. Beyond accuracy, the framework demonstrates

SUBMITTER: Rokaya M 

PROVIDER: S-EPMC12615596 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature