Raspberry-Pi Based Physical Media to Audio Conversion device for Visually Impaired Individuals

Authors

  • Aaryan Mehta Anand Niketan International School, Ahmedabad, Gujarat, India Author
  • Priyam Parikh Nirma University, Ahmedabad, Gujarat, India Author
  • Parth Shah Anant National University, Ahmedabad, Gujarat, India Author

DOI:

https://doi.org/10.32628/IJSRSET24114127

Keywords:

Visually Impaired People, Optical Character Recognition, Text-To-Speech Converters

Abstract

The proposed product is a device for real-time scanning and conversion of text from physical media to audio for the aid of visually impaired individuals. The focus of the project is to make a device which brings the experience of visually impaired individuals as close to that of the ordinarily abled/educated as possible when it comes to access to resources, books, and physical reading material. This device is targeted towards libraries, reading rooms, and schools for visually impaired individuals. The prototype is developed using a FDM 3D printer with PLA material and using a laser cutting machine with MDF material to allow for maximum customisability to meet the end-user’s needs. The proposed device is equipped with a Raspberry Pi 4B+, a camera, two pushbuttons, two potentiometers and a head-phone. A variety of image processing techniques, bundled with open-source optical character recognition (OCR) software and text-to-speech libraries, are used to capture and process images of book pages and convert them to audio files, all while maintaining a physical user interface which can be navigated autonomously by the visually challenged. The product is capable of handling over 200 fonts from 8pt to 36pt size. The product is successfully tested on 15 users for approximately 4000 words.

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Published

29-08-2024

Issue

Section

Research Articles

How to Cite

[1]
Aaryan Mehta, Priyam Parikh, and Parth Shah, “Raspberry-Pi Based Physical Media to Audio Conversion device for Visually Impaired Individuals”, Int J Sci Res Sci Eng Technol, vol. 11, no. 4, pp. 249–260, Aug. 2024, doi: 10.32628/IJSRSET24114127.

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