Designing Real-Time Hand Gesture Recognition Systems for Hearing Impaired: Combining CNNs with Human-Centric HCI Approaches

The human-computer interaction (HCI) encompasses a variety of interactions, including gestural ones. In HCI, gesture recognition refers to nonverbal movements that can be utilized for communication. Information can be delivered by using a system that recognizes human gestures. This is a major area of HCI that deals with user interfaces and device interfaces. The goal of gesture recognition is to capture certain gestures that are then picked up by a camera or other device.There are numerous applications where hand gestures can be utilized as a means of communication. People with a variety of disabilities, such as those suffering from strokes, hearing loss, or speech problems, can use it to communicate and take care of their fundamental needs. The goal of hand gesture recognition is to create more natural and intuitive ways for people to engage with computers while also opening up new avenues for interaction.Hand gestures have been the subject of numerous studies in the past. Various methods for carrying out hand gesture experiments were suggested by some studies. There are several methods available for extracting characteristics from photos in image processing, and artificial intelligence offers a variety of classifiers for classifying different kinds of data. An efficient method is needed to extract images and identify different small motions and movements from 2D and 3D hand gestures. The goal of this work is to create a hand gesture recognition system based on artificial intelligence that can recognize a hand gesture automatically using the test dataset. Within the class of deep learning neural networks is the convolution neural network.