A Lightweight I3D-Based Approach for Real-Time Brazilian Sign Language Recognition - Costa, Victor; Tavares, Leandro; Conceição, Ruhan; Agostini, Luciano; Santana, Brenda Salenave; Lebedeff, Tatiana Bolivar; Corrêa, Guilherme (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2025)
The demand for accessible technologies to support the deaf and hard-of-hearing community in Brazil is significant. However, many state-of-the-art deep learning models are too computationally intensive for practical, real-time applications. This study addresses this gap by proposing an efficient, lightweight pipeline approach for isolated Brazilian Sign Language (LIBRAS) recognition. We fine-tune a pre-trained Inflated 3D ConvNet (I3D) model on the MINDS-Libras dataset using an end-to-end methodology that operates directly on raw RGB videos, circumventing the need for heavy pre-processing steps like skeleton extraction. To ensure a realistic evaluation of the model’s generalization capabilities, we adopt a strict signer-independent protocol, where test subjects are completely unseen during training. Our proposed model achieves a competitive accuracy of 92.5% and is able to perform sign recognition in real-time, demonstrating strong performance comparable to more complex architectures. This work establishes a new, robust benchmark for signer-agnostic LIBRAS recognition, highlighting that an end-to-end approach can effectively balance high accuracy with the pipeline efficiency required for deployable, real-world accessibility tools.