BRIDGING COMMUNICATION GAPS USING AI-BASED GESTURE RECOGNITION: SIGNCONNECT.APP

SignConnect.App presents a practical assistive communication system aimed at supporting interaction between hearing- and speech-impaired individuals and non-sign language users. The application interprets static hand gestures and converts them into readable text and synthesized speech using computer vision techniques. Hand landmark extraction is performed through MediaPipe, while OpenCV, Tkinter, and pyttsx3 are employed for gesture processing, interface design, and offline speech output, respectively. The system is designed to recognize English alphabet gestures along with a limited set of commonly used words relevant to daily communication. In contrast to many existing solutions that rely on specialized hardware or internet-dependent processing, the proposed application operates entirely offline using a standard webcam. Experimental testing under typical indoor conditions indicates reliable recognition performance with minimal response delay, suggesting that the system offers a cost-effective and accessible solution for real-world assistive communication scenarios.