Analyzing Algorithms for Sign Language Modeling: A Tale of Development

The rapid advancement of assistive technologies has revolutionized accessibility for individuals with disabilities, particularly through innovations in sign language recognition and modeling. Sign languages, characterized by intricate combinations of hand gestures, facial expressions, and body movements, present unique computational challenges due to their multimodal and spatially complex nature. This study systematically reviews the evolution of algorithms in sign language recognition, examining the strengths and limitations of rule-based, machine learning, and deep learning approaches. The findings highlight the transformative potential of deep learning models, especially multimodal systems, while underscoring persistent challenges such as dataset scarcity, computational demands, and regional variability in sign languages. By synthesizing trends and identifying gaps, this research contributes actionable insights to the development of inclusive, efficient, and universal sign language recognition systems, supporting global accessibility goals.