ReviewJournal of imaging2026
Machine Learning as a Tool for the Development of Sign Recognition Systems: A Review.
Review in Journal of imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.