Advancing Hand-Sign-Language Detection for Mute and Deaf Communication Through Harris Corner Algorithm and Centroid Algorithm

Sign Language Recognition is an ever-expanding research domain with profound implications for deaf and mute individuals. This paper introduces an Android application designed to interpret sign languages into alphabets. Leveraging advanced image processing tools, including MatLab, image analysis, pattern recognition techniques, and gesture recognition, this project aims to provide an accessible and cost-effective solution for sign language comprehension. The project employs two key algorithms: the Harris Corner algorithm for precise image coordinate calculation and the Centroid Algorithm for image recognition. Through rigorous testing and experimentation, the application demonstrates exceptional accuracy in the detection, recognition, and interpretation of hand gestures. The application holds the promise of breaking down communication barriers between deaf and mute individuals and the hearing population. Offering a portable and affordable means of sign language interpretation, paves the way for more inclusive and accessible interactions, thereby fostering a society where communication knows no bounds. In a world where technology continues to advance, this research project represents a significant step towards bridging the gap and promoting equal communication opportunities for all.