Enhancing Accessibility and Autonomy for Visually Impaired Individuals Using AEyeD: An Assistive System

In this technological era, visually impaired individuals still face significant challenges in their daily lives, and still seek other's dependence. The Artificial Eye Decipher (AEyeD), aimed at increasing user independence and safety, combines face and gesture recognition; detection of vehicles and stairs; and a text-to-speech conversion feature. AEyeD implements Convolutional Neural Networks (CNN) for face and gesture recognition, and obj ect detection. The system recognizes approaching individuals, interprets gestures like handshakes, and identifies offered objects. Moreover, AEyeD identifies stairways and stationary automobiles, providing audio feedback via the ‘pyttsx3’ module for safer navigation. Image-based textual extraction and reading on demand are made possible by means of the ‘pytesseract’ module. With the help of test trials with patients suffering from Retinitis Pigmentosa (RP) in different scenarios on each task done by AEyeD, highlighting its potential to change the daily lives of visually impaired people by granting them the ability to navigate and engage with their environment independently.