Advancing sign language recognition: Neural networks and feature extraction optimization

This comparision shows advancements in sign language recognition using neural networks and optimized feature extraction techniques. It explores the integration of simultaneous gesture components through neural network models, contrasting single-stage classification approaches with methods employing separate hidden Markov model (HMM) channels. The study synthesizes findings from various classification schemes, incorporating both direct-measure devices and vision-based methodologies to enhance sign-level classification accuracy.