Machine learning in sign language: A comprehensive analysis and trend survey

Abstract

When exploring concepts such as sign language and machine learning, it is clear that this important, rapidly growing field bridges communication gaps, improves accessibility, and promotes inclusion for deaf and hard-of-hearing communities. Over the past five years, advanced machine learning has made significant progress, driven by innovative methods and emerging datasets. This paper presents recent advances in sign language translation, focusing on input methods including camera-based approaches and deep learning techniques. Key contributions from the reviewed works are identified and highlighted, showing trends in research on sign language recognition and translation. The metrics used to evaluate sign language recognition and translation are also examined. The critical features and differences of selected datasets relevant to sign language recognition are explained. The paper ends with a discussion of promising future research directions.