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@article{202884,
        author = {Krish Chabria and Shweta Kambre and Varad Kulat and Jaideep Khandagale},
        title = {AI-Powered Morse Code Communication System using Eye Blink Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9507-9509},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202884},
        abstract = {In modern healthcare and assistive communication systems, individuals suffering from paralysis, speech impairment, or severe physical disabilities often face significant challenges in communicating with others. Conventional communication methods require physical interaction, speech capability, or specialized expensive hardware, making them inaccessible in emergency or low-resource environments. To address this issue, this project presents an AI-powered real-time Morse code communication system using eye blink detection through computer vision techniques.
The proposed system utilizes a webcam to monitor eye movements and detect intentional blinks using facial landmark tracking and Eye Aspect Ratio (EAR) analysis. Short-duration blinks are interpreted as Morse code dots (.), while long-duration blinks represent dashes (-). The detected Morse code patterns are translated into alphabets and displayed as readable text in real time. The system integrates computer vision, facial landmark detection, digital image processing, and heuristic pattern recognition to provide a lightweight and efficient communication solution.
The methodology involves capturing live video frames, detecting facial landmarks using MediaPipe Face Mesh, calculating eye blink duration using geometric eye ratios, classifying blink signals into Morse code symbols, and decoding the generated Morse patterns into textual output. The system operates in real time and provides visual feedback through a graphical interface, making it suitable for assistive communication applications.
The proposed system can significantly improve communication for differently-abled individuals, ICU patients, and individuals with temporary speech disabilities. Its low-cost implementation, real-time processing capability, and non-invasive design make it highly suitable for healthcare assistance, accessibility technologies, and emergency communication systems.},
        keywords = {Computer Vision, Morse Code, Eye Blink Detection, MediaPipe, Assistive Technology, Human-Computer Interaction, Artificial Intelligence},
        month = {May},
        }