Paper Contents
Abstract
Sign language serves as the main communication method for millions of individuals with hearing impairments across the globe. Nonetheless, a significant communication gap persists between those who use sign language and the broader population. Machine intelligence-based sign language recognition (SLR) systems have surfaced as effective solutions for overcoming this challenge 1, 2. This survey paper examines the various existing SLR methods, with an emphasis on machine learning and deep learning techniques, datasets, tools, challenges, and applications 3, 4. It also explores future avenues for improving accuracy, robustness, and the implementation of real-time solutions 5. In addition, the survey highlights integration with wearable devices, cross-lingual sign translation, and mobile-based recognition systems, which are gaining momentum in real-world accessibility solutions 6.
Copyright
Copyright © 2025 Carol I. This is an open access article distributed under the Creative Commons Attribution License.
