ConvAtt Network: A Low Parameter Approach For Sign Language Recognition

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Gaston Gustavo Rios, Pedro Dal Bianco, Franco Ronchetti, Facundo Manuel Quiroga, Santiago Ponte Ahón, Oscar Stanchi, Waldo Hasperué

Published: 2024, Last Modified: 26 Sept 2025J. Comput. Sci. Technol. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Despite recent advances in Large Language Models in text processing, Sign Language Recognition (SLR) remains an unresolved task. This is, in part, due to limitations in the available data. In this paper, we investigate combining 1D convolutions with transformer layers to capture local features and global interactions in a low-parameter SLR model. We experimented using multiple data augmentation and regularization techniques to categorize signs of the French Belgian Sign Language. We achieved a top-1 accuracy of 42.7% and a top-10 accuracy of 81.9% in 600 different signs. This model is competitive with the current state of the art while using a significantly lower number of parameters.