HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation
This work addresses data scarcity in sign language recognition, offering computationally efficient methods that could benefit researchers and developers in this domain, though it appears incremental as it builds on existing augmentation techniques.
The paper tackles the problem of insufficient training data for Sign Language Recognition (SLR) by introducing a lightweight sign generation model based on CMLPe and a synthetic data pretraining approach, which improves recognition accuracy and achieves new state-of-the-art results on the LSFB and DiSPLaY datasets.
Sign Language Recognition (SLR) models face significant performance limitations due to insufficient training data availability. In this article, we address the challenge of limited data in SLR by introducing a novel and lightweight sign generation model based on CMLPe. This model, coupled with a synthetic data pretraining approach, consistently improves recognition accuracy, establishing new state-of-the-art results for the LSFB and DiSPLaY datasets using our Mamba-SL and Transformer-SL classifiers. Our findings reveal that synthetic data pretraining outperforms traditional augmentation methods in some cases and yields complementary benefits when implemented alongside them. Our approach democratizes sign generation and synthetic data pretraining for SLR by providing computationally efficient methods that achieve significant performance improvements across diverse datasets.