Real Time Object Recognition with Voice Guided Navigation for Visually Impaired using OpenCV

Navigating in the open surroundings independently for the visually impaired individuals is quite a difficult task. There are many troublesome conditions that this community faces, like understanding spatial layouts, making a safe move, and identifying the obstacles. Also, there are many existing solutions, like handheld devices and wearable sensors, that are aiming to address this problem, but they often fail because of varied reasons like accuracy, not being user-friendly, affordability, and no multilingual support. This study presents the systematic approach of implementing the Real-Time Object Recognition with Voice Guided Navigation using OpenCV (RT-ORVGN). The Proposed model processes the real-time video from the user, identifies the object, gives voice assistance, which will be extremely helpful to users, especially for the visually impaired, and provides navigation to enhance their independence, and it also lets users know the distance between the user and the object to have a clear awareness and idea about the environment. Built using advanced deep learning algorithms and various pre-trained models, which helps in identifying objects with high accuracy and precision under varying conditions. In addition, many more features are added like multilingual support, where users can feel highly comfortable using the system, and providing a settings page where users can save their personal details. With the combination of cutting-edge computer vision techniques, this model (RT-ORVGN) delivers a highly accurate, efficient, and pragmatic solution to empower visually impaired individuals in their daily lives.