Gesture recognition for hearing impaired people using an ensemble of deep learning models with improving beluga whale...

Sign language (SL) is the linguistics of speech and hearing-impaired individuals. The hand gesture is the primary model employed in SL by speech and hearing-challenged people to talk with themselves and ordinary persons. At present, hand gesture detection plays a vital part, and it is commonly employed in numerous applications worldwide. Hand gesture detection systems can aid in transmission between machines and humans by aiding these sets of people. Machine learning (ML) is a subdivision of artificial intelligence (AI), which concentrates on the growth of a method. The main challenge in hand gesture detection is that machines do not directly understand human language. A standard medium is required to facilitate communication between humans and machines. Hand gesture recognition (GR) serves as this medium, enabling commands for computer interaction that specifically benefit hearing-impaired and elderly individuals. This study proposes a Gesture Recognition for Hearing Impaired People Using an Ensemble of Deep Learning Models with Improving Beluga Whale Optimization (GRHIP-EDLIBWO) model. The main intention of the GRHIP-EDLIBWO model framework for GR is to assist as a valuable tool for developing accessible communication systems for hearing-impaired individuals. To accomplish that, the GRHIP-EDLIBWO method initially performs image preprocessing using a Sobel filter (SF) to enhance edge detection and extract critical gesture features. For the feature extraction process, the squeeze-and-excitation capsule network (SE-CapsNet) effectively captures spatial hierarchies and complex relationships within gesture patterns. In addition, an ensemble of classification processes, such as bidirectional gated recurrent unit (BiGRU), Variational Autoencoder (VAE), and bidirectional long short-term memory (BiLSTM) technique, is employed. Finally, the improved beluga whale optimization (IBWO) method is implemented for the hyperparameter tuning of the three ensemble models. To achieve a robust classification result with the GRHIP-EDLIBWO approach, extensive simulations are conducted on an Indian SL (ISL) dataset. The performance validation of the GRHIP-EDLIBWO approach portrayed a superior accuracy value of 98.72% over existing models.