FingerAlphaNet: Static Sign Language Alphabet Recognition with Attention-Enhanced Convolutional Neural Networks for Inclusive Human–Computer Interaction

Sign Language Alphabet Recognition (SLAR) is a critical component of inclusive Human–Computer Interaction (HCI), particularly for facilitating communication with Deaf and Hard-of-Hearing (DHH) individuals. The paper at hand proposes an attention-enhanced lightweight Convolutional Neural Network (CNN) architecture for interpretable SLR, exploiting the Convolutional Block Attention Module (CBAM) to improve both classification performance and model transparency. The introduced method is assessed on the Sign Language MNIST dataset, where the integration of attention mechanisms improves classification accuracy compared with the corresponding attention-free CNN configurations. Additionally, qualitative attention maps are presented to visualize the spatial regions emphasized by the model during classification, complemented by a quantitative attention-perturbation analysis assessing the functional relevance of these regions. Furthermore, a comprehensive analysis is conducted regarding the computational cost introduced by the CBAM integration, demonstrating a favorable trade-off between accuracy, interpretability, and model complexity. A comparative analysis with representative methods evaluated on the same dataset is also presented, contextualizing the proposed architectures in terms of recognition performance and computational efficiency. This study underscores the potential of attention mechanisms for developing more transparent and inclusive AI systems using static SLAR, providing a foundation for future real-world HCI and assistive communication applications. From an assistive technology and occupational therapy perspective, such recognition systems may support the development of accessible Augmentative and Alternative Communication (AAC) interfaces aimed at reducing communication barriers and facilitating participation of DHH individuals in everyday activities.