Transforming Sign Language Interpretation with VGG16 and ResNet50 Deep Learning Models

Sign language recognition (SLR) is crucial for effective communication with individuals who are deaf or hard of hearing. This study explores the application of two prominent deep learning models, VGG16 and ResNet50, in SLR tasks. By leveraging the VGG16 and ResNet50 frameworks, we achieved impressive accuracy rates of 99.92% and 99.95%, respectively, in detecting and interpreting sign language gestures. The exceptional performance of these models in accurately analyzing hand movements and gestures highlights their potential in enhancing communication for sign language users. Our research employs cutting-edge deep learning methods to advance SLR systems, paving the way for improved inclusivity and accessibility in communication.