AI Powered Glasses for Visually Impaired Person
Nirav Satani1, Smit Patel2, Sandip Patel3

1Nirav Satani*, Information Technology, Parul University, Vadodara, India.
2Smit Patel, Information Technology, Parul University, Vadodara, India.
3Sandip Patel, Information Technology, Parul University, Vadodara, India. 

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 316-321 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3565079220/2020©BEIESP | DOI: 10.35940/ijrte.B3565.079220
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Abstract: This dissertation presents a system that can assist a person with a visual impairment in both navigation and movability. Meanwhile, number of solutions are available in current time. We described some of them in the later part of the paper. But to date, a reliable and cost-effective solution has not been put forward to replace the legacy devices currently used in mobilizing on a daily basis for people with a visual impairment. This report first examines the problem at hand and the motivation behind addressing it. Later, it explores relative current technologies and research in the assistive technologies industry. Finally, it proposes a system design and implementation for the assistance of visually impaired people. The proposed device is equipped with hardware like raspberry pi processor, camera, battery, goggles, earphone, power bank and connector. Objects will be captured with the help of camera. Image processing and detecting would be done with the help of deep learning, R-CNN like modules on the device itself. However, final output would be delivered by the earphone into the visually impaired person’s ear. The research work contains the methodology and the solutions of above mention problem. The research works can be used in practical use cases, for visually impaired person. The system proposed in this project includes the use of a region based convolutional neural network as well as the use of a raspberry pi for processing the image data. System includes tesseract library of programming language python for OCR and give output to the user. The detailed methodology and result are elaborated later in this paper. 
Keywords: OCR, R-CNN, Transfer learning.