Image to Speech Conversion using CV to Help Visually Challenged People

Abstract

This work aims to assist the visually impaired people for reading a text material and detect objects in their surroundings. The input is taken in the form of an image captured from the web camera. This image is then processed either for the purpose of text reading or for object detection based on user choice. The main aim of this project is to build a system that detects objects from the image or a stream of images given to the system in the form of previously recorded video or the real time input from the camera. Bounding boxes will be drawn around the objects that are being detected by the System. The system will also classify the object to the classes the object belongs. Python programming and a machine Learning technique named yolo (you only look once) algorithm using convolutional neural network is used for the object detection. The smart blind navigation is fill gap, providing accurate and contextually rich information about the environment around the user current location, and simplifying the navigation and increasing the overall accuracy of the System. Preventing the user from dangerous locations. They have very little information on self-velocity objects, direction which is essential for travel. The navigation systems is costly which is not affordable by the common blind people. The navigation system are heavy complicated to operate.