Machine Learning as a Tool for the Development of Sign Recognition Systems: A Review

Abstract

Hearing loss represents a global challenge, affecting the ability of the deaf community to communicate, study, and work. Currently, the World Health Organization reports that more than 430 million people live with disabling hearing loss. Among rehabilitation alternatives, sign language is one of the most widely adopted; however, effective communication between deaf individuals and the hearing population remains a barrier. To help bridge this communication gap, the development of technological tools has been promoted, particularly Sign Recognition Systems (SRSs). The purpose of this review is to provide an integrative analysis of the current state of SRS research motivated by sign language teaching and learning applications, identifying dominant methodological approaches across the full pipeline—from data capture and feature extraction to classification and technological deployment—and highlighting the persistent gap between high experimental performance and real-world accessibility. This literature review analyzes 55 articles, selected from an initial pool of 161 records identified in journals indexed in the Scopus database between 2020 and 2025. The analysis revealed that these systems are generally structured in two stages: feature extraction and classification. Finally, despite the high performance achieved by SRS, a gap remains in transferring these systems into technologies that can be effectively used by end users.