CNN architecture based on VGG16 model for SIBI sign language Available to Purchase

This paper discusses the importance of communication for human interaction, especially for individuals with hearing impairments who rely on sign language. However, learning sign language is not easy, and not many people are familiar with it. To bridge the communication gap between deaf individuals and the general public, this study focuses on developing a deep convolutional neural network (CNN) model to recognize sign language patterns, specifically SIBI Sign Language used for formal activities. The VGG16 architecture is employed, which has been trained on a sign language dataset with 1440 images. The results show an accuracy of over 98.32% in training data. This research demonstrates how technology, particularly deep learning and computer vision, can help solve communication problems and improve accessibility for individuals with hearing impairments.