Sign Language Recognition Using Artificial Rabbits Optimizer with Siamese Neural Network for Persons with Disabilities

Abstract

Sign language recognition is an effective solution for individuals with disabilities to communicate with others. It helps to convey information using sign language. Recent advances in computer vision (CV) and image processing algorithms can be employed for effective sign detection and classification. As hyperparameters involved in Deep Learning (DL) algorithms considerably affect the classification results, metaheuristic optimization algorithms can be designed. In this aspect, this manuscript offers the design of Sign Language Recognition using Artificial Rabbits Optimizer with Siamese Neural Network (SLR-AROSNN) technique for persons with disabilities. The proposed SLR-AROSNN technique mainly focused on the recognition of multiple kinds of sign languages posed by disabled persons. The goal of the SLR-AROSNN technique lies in the effectual exploitation of CV, DL, and parameter tuning strategies. It employs the MobileNet model to derive feature vectors. For the identification and classification of sign languages, Siamese neural network is used. At the final stage, the SLR-AROSNN technique makes use of the ARO algorithm to get improved sign recognition results. To illustrate the improvement of the SLR-AROSNN technique, a series of experimental validations are involved. The attained outcomes reported the supremacy of the SLR-AROSNN technique in the sign recognition process.