Motion-Based Gesture Recognition for Emoji Communication in Deaf and Mute Individuals.

Many deaf or hard-of-hearing individuals rely on sign language, which uses gestures rather than sounds to convey meaning. This paper presents a system for motion-based gesture recognition to facilitate emoji communication for deaf and mute individuals. This proposed System is designed based on computer vision. The system, proposed that uses OpenCV, Python, and MediaPipe, detects and classifies hand gestures with high accuracy. Experimental results demonstrate the system’s effectiveness in recognizing seven gestures: peace sign, call me, thumbs up, rock on, open palm, hello, and pointing. With this approach deaf and mute people able to converse with easiness of displayed gestures with the text added to recognize during the conversation. Deaf and mute people can find Hand motion gesture recognition based system as an alternative to using a person to transmit their sentiments thus makes them more easily communicate. The system achieves an average accuracy of 88.6% across all tested gestures, with real-time detection capabilities. This research enhances communication accessibility for deaf and mute individuals through intuitive gesture-to-emoji translation.