Integrating Deep CNN Models for Multilingual Sign Language Recognition: A SignLink-Based Approach for Bengali and English

Sign language is a vital medium of communication
for people who are deaf or hard of hearing. While much
research has addressed widely used systems such as American
Sign Language (ASL) and British Sign Language (BSL), Bengali
Sign Language (BdSL) remains far less explored. To help close
this gap, this work proposes a bilingual Bengali and English
sign language recognition system that applies deep learning for
stronger multilingual gesture interpretation. Two publicly avail
able datasets were merged. The first contains English letters from
A to Z, digits from 0 to 9, and a space symbol, while the second
holds 38 Bengali alphabet gestures. After the datasets were
combined and augmented to balance the class distribution, the
final collection held 75 classes and 112,493 images in total. Eight
deep learning models were evaluated, comprising six pre-trained
architectures and two custom networks. Among them, AlexNet
gave the best standalone test accuracy of 96.99%. To raise
accuracy further and reduce overfitting, a hybrid model named
SignLink was built by joining AlexNet with MobileNetV2 and
Xception, both of which generalize well. The hybrid reached a
test accuracy of 98.93%, surpassing every individual architecture.
The proposed system shows reliable bilingual recognition and
supports inclusive communication technology for linguistically
diverse and low resource settings.