Vision Assist System based on Deep Learning Using Object Detection and Text Recognition with Voice Alert

For blind people, recognizing a product in their daily routine is critical, so we proposed a method to identify products in their daily routine using a camera. A mobility method is employed to spot item of worry from the photo by advising person about recognized objects in order to separate an object from unnecessary background. The current project's goal is to model an object detector for visually impaired people and other commercial purposes by recognizing objects at a specific distance. Old techniques for object recognition required a large amount of training data, which takes more time and is quite complicated. Detection is employed in a variety of situations. Traditional methods of object detection rely on a large number of datasets, and training these datasets takes a long time. Training tiny or unnoticed objects is a more difficult task. People's minds and visual systems detect objects more accurately and quickly in real time, and they have conscious thoughts when detecting obstacles. Because of the abundance of data available, in addition to more advanced technologies and better working algorithms, classification and detection of multiple objects within the same frame has become simple and accurate. The project's main objective is to develop and implement a real-time object recognition system using a real-time camera. We can use Python as the front end to implement the system in real time. The experimental results show that the proposed system improves product identification accuracy.