A Framework for Lightweight, Edge-Based Recognition of Dynamic American Sign Language Using Temporal Learning Models
A theoretical and methodological framework for evaluating lightweight machine learning models for dynamic ASL recognition under CPU-dependent constraints is proposed, establishing the conceptual foundations, modeling assumptions, and evaluation criteria necessary to determine whether lightweight classifiers can approxi...