Enhancing Sign Language Recognition and Translation with Deep Learning: A CNN-Based Approach

Sign language translation plays an important role in filling the communication gap that exists between the hearing able-bodied individuals and the ones who are deaf or hearing-impaired. The research undertaken for this paper uses CNNs to achieve high accuracy automated sign language recognition and translation. It processes the images through a CNN model, classified, and interpreted as a set of signs that provide an efficient gestural analysis of labeled data for the sign language. The methodology mostly used here revolves around preprocessing techniques, optimized CNN architectures, and robust training strategies in order to obtain a precise translation. This research work has made it clear that deep learning methods such as CNN are scalable and viable solutions to sign language translation applications, making it easier for different user groups.