AI-Based Approaches for Brazilian Sign Language Recognition: A Systematic Literature Review: Insights on Methods, Metrics, and Resources for LIBRAS Recognition - Costa, Victor; Tomaszewski, João Pedro; Pereira, Lucas; Santana, Brenda Salenave; Corrêa, Guilherme (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the Web, 2025)
This Systematic Literature Review (SLR) investigates the application of Artificial Intelligence (AI), specifically with Computer Vision (CV) and Natural Language Processing (NLP) techniques for Brazilian Sign Language (LIBRAS) recognition, translation, and interpretation. Following the PRISMA protocol and using the Parsifal tool to support protocol definition and study management, 77 studies were initially identified. After removing duplicates and applying exclusion criteria, 23 studies were selected for in-depth analysis. The review addresses three research questions: (i) AI-based solutions currently employed for LIBRAS recognition, (ii) reported performance metrics and evaluation results, and (iii) datasets used for training and testing, along with their characteristics and limitations. The findings highlight advances in deep learning architectures and computer vision pipelines, while revealing persistent challenges such as limited integration of non-manual features, scarcity of large-scale and multimodal datasets, difficulties in continuous sign recognition, heterogeneous annotation practices, and inadequacy of common bilingual evaluation metrics for capturing syntactic and semantic differences between LIBRAS and Portuguese. Moreover, deployment-related factors, including inference time and computational cost, remain underexplored. These insights provide guidance for future research toward more accurate, efficient, and inclusive LIBRAS recognition systems.