Gesture Intelligence: VGG16 and ResNet50 in Sign Language Interpretation

Sign language recognition (SLR) is essential for facilitating communication for individuals who are deaf or hard of hearing. This study explores the application of two prominent deep learning models, VGG16 and ResNet50, in the context of SLR tasks. By implementing the VGG16 and ResNet50 architectures, we achieved impressive accuracy rates of 99.92% and 99.95%, respectively, in recognizing sign language gestures. Our findings demonstrate the effectiveness of these models in accurately interpreting hand movements and gestures, thereby supporting seamless communication for sign language users. This research leverages advanced deep learning techniques to enhance SLR systems, offering significant potential for improved accessibility and inclusive communication.