<![CDATA[Sign-language recognition (SLR) plays a pivotal role in enhancing communication accessibility and fostering the inclusion of deaf communities. Despite significant advancements in SLR systems, challenges such as variability in sign language gestures, the need for real-time processing, and the complexity of capturing spatiotemporal dependencies remain unresolved. This study aims to address these limitations by proposing an advanced framework that integrates deep learning and machine learning techniques to optimize sign language recognition systems, with a focus on the Indian Sign Language (ISL) dataset. The framework leverages MobileNetV3 for feature extraction, which is selected after rigorous evaluation against VGG16, ResNet50, and EfficientNet-B0. MobileNetV3 demonstrates superior accuracy and efficiency, making it optimal for this task. To enhance the model's ability to capture complex dependencies and contextual information, multi-head self-attention (MHSA) was incorporated. This process enriches the extracted features, enabling a better understanding of sign language gestures. Finally, LightGBM, a gradient-boosting algorithm that is efficient for large-scale datasets, was employed for classification. The proposed framework achieved remarkable results, with a test accuracy of 98.42%, precision of 98.19%, recall of 98.81%, and an F1-score of 98.15%. The integration of MobileNetV3, MHSA, and LightGBM offers a robust and adaptable solution that outperforms the existing methods, demonstrating its potential for real-world deployment. In conclusion, this study advances precise and accessible communication technologies for deaf individuals, contributing to more inclusive and effective human-computer interaction systems. The proposed framework represents a significant step forward in SLR research by addressing the challenges of variability, real-time processing, and spatiotemporal dependency. Future work will expand the dataset to include more diverse gestures and environmental conditions and explore cross-lingual adaptations to enhance the model’s applicability and impact.]]>
