Abstract

People with hearing impairments and those who are non-verbal frequently struggle to express themselves and comprehend others in a world that is largely based on verbal communication. Technology has become a potent ally in bridging this communication gap. this paper introduces a novel method for hand gesture recognition with the goal of empowering nonverbal and hearing-impaired people by giving them an easy-to-use means of communication. Hand gesture recognition is a crucial research area in human-computer interface due to its flexibility and user-friendliness. It is used to communicate information among disabled individuals or control devices. However, challenges include illumination variation, nonuniform backgrounds, hand size and shape differences, and high interclass similarities between hand gesture poses, making it challenging to develop an efficient technique. Google's MediaPipe framework is a solution that enables real-time hand gesture recognition for hearing impaired and non-verbal individuals. This system uses computer vision and machine learning to interpret hand gestures and convert them into meaningful actions or messages. The system identifies a wide range of common hand gestures, which can be mapped to specific actions or words. The system can be implemented on various devices, making it accessible and versatile. This research highlights the potential of MediaPipe for enhancing the lives of hearing impaired and non-verbal individuals, bridging the communication gap, fostering inclusivity, and improving their overall quality of life. This technology has the potential to completely change how these people interact with their environment by utilizing real-time hand tracking and gesture recognition capabilities for enabling meaningful communication process.