A Smart Shopping Assistant at Super Shop for Visually Challenged Persons Utilizing Machine Learning Approach

This research develops an Assistive System to help visually impaired individuals shop in super shops. The system combines machine learning, real-time image processing, and text-to-speech technologies to make shopping easier. A custom dataset with 30 categories of essential food and wearable items is used. The YOLOv8 pre-trained model, known for its strong performance, handles object detection and recognition, achieving 82.7% accuracy. When an object is detected, Google's Text- To-Speech (GTTS) technology converts visual information into spoken words, giving immediate feedback to the user. The research focuses on data collection for training, real-time object detection, and converting recognized items into speech. By addressing the challenges faced by visually impaired individuals, this study shows how image processing and machine learning can be used in assistive technologies. The system is designed to improve the quality of life for visually impaired people by making shopping more independent and confident. It offers a practical solution to help them navigate and shop in super shops, fulfilling an important need in their daily lives.