Published Paper Details:

The Morse Code Detection System using eye blink and sound detection is an innovative, web-based communication framework designed to assist individuals with speech and motor impairments. This system transforms involuntary biological signals such as eye blinks and sound pulses into meaningful Morse code, which is then translated into human-readable text. By integrating modern browser-based machine learning technologies including TensorFlow.js and MediaPipe Face Mesh, along with a cloud-native backend powered by Convex, the system enables real-time blink detection, signal classification, data storage, and communication history management. Traditional assistive technologies often require specialized hardware or costly infrastructure, making them inaccessible to many individuals. In contrast, this system operates entirely within a standard browser environment and uses only a webcam and microphone, promoting accessibility, affordability, and ease of deployment. The architecture supports calibration for personalized sensitivity, session-based logging for user communication patterns, and real-time signal visualization. This project demonstrates how modern web technologies, combined with intelligent detection algorithms, can facilitate reliable communication for users with paralysis, ALS, or other conditions affecting motor capabilities. The system not only decodes Morse code signals accurately but also ensures session persistence, user customization, and extensibility toward future enhancements such as advanced sound detection and predictive communication models.