Vision to Voice: An Advanced Blind Assistance System Integrating YOLOv3 and OCR Technologies for Enhanced Mobility

The rise in need for assistive technologies has led to the development of AI-empowered solutions for better accessibility for visually impaired persons. The proposed system is the use of a real-time object and text detection application using a combination of deep learning and speech synthesis techniques to enhance mobility and independence. The system utilizes Tesseract OCR for text recognition and pyttsx3 for TTS for better interfacing with the environment. YOLOv3-based object detection towards improving environmental awareness involves real-time identification of objects, with confidence thresholding and NMS serving to enhance robustness of the overall detection by curbing false positives. Further, the distance estimation module helps in mobility as it provides information on how far one is situated from an object. This architecture is a hardware-independent, lowlatency, and highly scalable solution compared to conventional assistive systems; thus, such systems are more harmonized for practical applications. Experimental findings exhibited its very high accuracy, confident standing across situations, and enhanced user experience rendering it with huge potential for granting enhanced independence and safety for the visually challenged user base.