Glove-translator system for sign language based on convolutional neural networks (CNN): A systematic literature review

Abstract

This systematic literature review (SLR) evaluated the design approaches, technical effectiveness, and existing gaps in the implementation of glove-based sign translator systems that employ Convolutional Neural Networks (CNNs). The PRISMA methodology was used to organize and document the study selection and evaluation process. The initial search of the Scopus and Web of Science databases identified 70 publications; after removing duplicates and applying inclusion and exclusion criteria, the final sample compromised 11 studies with thematic relevance and adequate methodological quality. Based on this selection, the most commonly used CNN architectures were examined and their performance was compared, mainly in terms of accuracy. The findings indicate the integration of these architectures with proper development. Furthermore, signal preprocessing, particularly noise filtering and normalization, improves the stability and consistency of the collected data. Despite these advances, relevant limitations persist, including the lack of hardware and evaluation metric standardization, the limited availability of public datasets for specific sign languages, and the high implementation costs. These factors reduce reproducibility and hinder application in real-world contexts. Consequently, the need for standardized datasets, common evaluation protocols, and low-cost, high-efficiency solutions is emphasized to promote practical deployment and communicative inclusion.