Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals

Sign language (SL) is a non-verbal language applied by deaf and hard-of-hearing individuals for daily communication between them. Studies in SL recognition (SLR) have recently become essential developments. The current successes present the base for upcoming applications to assist the combination of deaf and hard-of-hearing people. SLR could help break down the obstacles for SL users in the community. In general, glove-based and vision-based techniques are the dual major types measured for SLR methods. Several investigators presented various techniques with significant development by deep learning (DL) models in computer vision (CV) and became performed to SLR. This study presents a novel Harris Hawk Optimization-Based Deep Learning Model for Sign Language Recognition (HHODLM-SLR) technique. The HHODLM-SLR technique mainly concentrates on the advanced automatic detection and classification of SL for hearing and speech-impaired individuals. Initially, the image pre-processing stage applies bilateral filtering (BF) to eliminate noise in an input image dataset. Furthermore, the ResNet-152 model is employed for the feature extraction process. The bidirectional long short-term memory (Bi-LSTM) model is used for SLR. Finally, the Harris hawk optimization (HHO) approach optimally adjusts the Bi-LSTM approach's hyperparameter values, resulting in more excellent classification performance. The efficiency of the HHODLM-SLR methodology is validated under the SL dataset. The experimental analysis of the HHODLM-SLR methodology portrayed a superior accuracy value of 98.95% over existing techniques.