Enhanced feature fusion with hand gesture recognition system for sign language accessibility to aid hearing and speech impaired individuals.

In the modern world, individuals with intellectual or communication disabilities face significant challenges in communicating with others. To reduce their communication difficulties, a communication system is designed and developed to convert sign language into text and speech. Dynamic hand gesture recognition (HGR) is a preferred option that focuses on human-computer interactions (HCI). HGR investigation is obtaining increasing attention from investigators globally. Also, regular application in day-to-day life, gesture recognition (GR) is beginning to enter education, virtual reality, automotive, mobile devices, and so on. Owing to the massive growth in artificial intelligence (AI), computer vision (CV)-based GR systems are the most extensively researched field recently. This paper presents a Feature Fusion-based Hand Gesture Recognition for Sign Language Accessibility using the Tornado Optimisation Algorithm (FFHGR-SLATOA) model to aid hearing- and speech-impaired people. The aim is to develop an innovative deep learning-based HGR model to enhance communication accessibility for hearing- and speech-impaired individuals. The image pre-processing stage begins with median filtering (MF) to improve image quality by removing noise. Furthermore, the fusion of ConvNeXt Base, VGG16, and EfficientNet-V2 techniques is employed for the feature extraction process. Moreover, the FFHGR-SLATOA approach employs the deep belief network (DBN) model for classification. Finally, the tornado optimization algorithm (TOA) model is implemented for the parameter tuning process. The experimental analysis of the FFHGR-SLATOA approach is performed under the GR dataset. The comparison study of the FFHGR-SLATOA approach portrayed a superior accuracy value of 99.14% over existing models.