Silent Speaker's Communicator: a Gesture-to-Text Protocol for Assistive Communication

The proposed Silent Speaker Communicator is an assistive communication system designed to support individuals with speech impairments in interacting more effectively by employing hand gesture recognition technology. The system begins by capturing real-time gestures using a standard camera, providing a contactless and user-friendly interface. Each video frame undergoes image preprocessing steps, including noise reduction, normalization, and segmentation, to accurately separate the hand region from the background. Following preprocessing, the system conducts feature extraction, identifying key visual characteristics like hand shape, finger direction, and spatial arrangements. These features create a concise and informative representation of the gesture, which helps to reduce computational demands while enhancing the accuracy of gesture recognition. The resulting feature set is then sent to a machine learning classification model that has been trained using labeled gesture data. This model evaluates the input features and classifies the gesture into the most suitable category based on the patterns it has learned.