Integrating deep CNN models for multilingual Sign Language recognition: A SignLink-based approach for Bengali and English

Sign language serves as a vital medium of communication for individuals with hearing or speech impairments. While considerable research has focused on the recognition of widely used sign languages such as American Sign Language (ASL) and British Sign Language (BSL), Bengali Sign Language (BdSL) remains significantly underexplored. To address this gap, a bilingual Bengali-English sign language recognition system is proposed, leveraging deep learning techniques for enhanced multilingual gesture interpretation. Two publicly available datasets were combined—one comprising English letters (A–Z), digits (0–9), and a space symbol, and the other consisting of 38 Bengali alphabet gestures. Following dataset merging and augmentation to balance class distributions, the final dataset contained 75 classes and a total of 112,493 images. Eight deep learning models were evaluated, including six pre-trained architectures and two custom-designed networks. Among these, AlexNet demonstrated the highest standalone test accuracy of 96.99%. To further improve performance and mitigate overfitting, a hybrid model named SignLink was developed by integrating AlexNet with MobileNetV2 and Xception—two models exhibiting strong generalization. The hybrid system achieved a test accuracy of 98.93%, outperforming all individual architectures. The proposed system demonstrates robust bilingual sign language recognition capabilities, contributing to inclusive and accessible communication technologies in linguistically diverse and low-resource settings.