Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Sign Language Recognition for Hearing- and Speech-impaired Persons

Abstract

Sign language is commonly used to interact with people who have speech and hearing disorders. Sign language was exploited for interacting with people having developmental impairments who have some or no communication skills. Communication using Sign language has become a fruitful means of interaction for speech- and hearing-impaired people. The hand gesture recognition technique is useful for dumb and deaf people by using convolutional neural networks (CNNs) and human–computer interface for recognizing the static indication of sign language. Therefore, this study presents a new Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Intelligent Sign Language Recognition (SCSO-DWAESLR) technique for hearing- and speech-impaired persons. In the presented SCSO-DWAESLR technique, computer vision and CNN concepts are utilized for identifying sign languages to aid the interaction of hearing- and speech-impaired persons. The SCSO-DWAESLR method makes use of the Inception v3 model for the feature map generation process. In addition, the DWAE classifier is utilized for the recognition and classification of different kinds of signs posed by hearing- and speech-impaired persons. Finally, the hyperparameters related to the DWAE classifier are optimally chosen by using the SCSO algorithm. For exhibiting the effectual recognition outcomes of the SCSO-DWAESLR technique, a detailed experimental analysis was performed. The comparative outcome highlights the superior recognition performance of the SCSO-DWAESLR method over existing techniques under several evaluation metrics.