Breaking Barriers with AI: The Evolution and Challenges of Automated Sign Language Recognition

Article

Abstract

Communication remains a significant challenge for individuals with hearing impairments and speech-related disabilities, especially when others are not familiar with sign language. Developing technologies that facilitate seamless communication for these individuals is crucial to promote equality for disabled people and accessibility for all. Sign language recognition systems have emerged as a promising solution, typically implemented using a hardware or software-based approach. Hardware solutions, such as sensor-equipped gloves, often pose usability and cost barriers, making them less appealing for widespread adoption. In contrast, software-driven approaches using artificial intelligence (AI), deep learning (DL) and machine learning (ML) offer a more practical and scalable alternative. This paper provides a complete review of recent developments in AI-based sign language recognition systems, with a particular attention towards deep learning architectures such as Convolution Neural Networks (CNNs). The aim is to evaluate current methodologies, highlight their strengths and limitations, and identify potential directions for future research to improve communication technologies for hearing-impaired people.