A Sign Language Recognition using Improved Grey Wolf Optimization based neural networks

Sign language was developed to enable the deaf and hard of hearing community to communicate with society and convey information. It is the primary means by which they can interact with each other, as well as with the general population. The automatic gesture recognition system described in this paper uses Gray Wolf Optimization (GWO) to identify the most relevant features from a set of gesture images, with the aim of improving model performance. The main steps of the system are explained, including image preprocessing, feature extraction, GWO-based feature selection, classifier training, and evaluation. The proposed model achieved an accuracy of 99.9%.