Design and Implementation of Smart Book Reader for the Blind

  • Gayathri RajendrababuEmail author
  • Rajesh Kannan Megalingam
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


This research work suggests a technique to execute an OCR based reader for the substantial number of visually debilitated. Considering how there is a large number of such people out there, we need to come up with a novel method that can be affordable as well as provides a good output. These book readers can be implemented in various ways. Here we talk around an OCR based system that is executed utilizing the product Tesseract. Later the recognized text becomes audio using TTs engines. The module utilizes a Raspberry Pi model 3B and a camera. The camera is for getting an image of the content. The image is then preprocessed before getting loaded into OCRs. The preprocessing section fuses binary images, noise removal, skew correction, division and feature extraction.


Compliance with Ethical Standards

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.


  1. 1.
    Megalingam, R.K., Vishnu, S., Sasikumar, V., Sreekumar, S.: Autonomous path guiding robot for visually impaired people. Adv. Intell. Syst. Comput. 768, 257–266 (2019)Google Scholar
  2. 2.
    Rao, S.N., Dsilva, C., Parthasarathy, V.: Evaluation of a smartphone keyboard for the visually challenged. In: 2nd IEEE International Conference on Electrical, Computer and Communication Technologies 2017 (ICECCT 2017) (2017)Google Scholar
  3. 3.
    Sonth, S., Kallimani, J.S.: OCR based facilitator for the visually challenged. In: 2017 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) (2017)Google Scholar
  4. 4.
    Pascolini, D., Mariotti, S.P.: Global estimates of visual impairment. Br. J. Ophthalmol. 96, 614–618 (2012)., Scholar
  5. 5.
    Rao, N.V., et al.: Optical character technique recognition algorithms. J. Theo. Appl. Inf. Technol. 83(2) (2016)Google Scholar
  6. 6.
    Baboo, D.S.S., et al.: Embedded optical braille recognition on tamil braille system using raspberry pi. Int. J. Comput. Technol. Appl. 5(4), 1566–1574 (2007)Google Scholar
  7. 7.
    Merino-Gracia, C., et al.: A head-mounted device for recognizing text in natural scenes. In: International Workshop on Camera-Based Document Analysis and Recognition, pp. 29–41. Springer, Heidelberg, (2011)CrossRefGoogle Scholar
  8. 8.
    Velumurugan, D., et al.: Hardware implementation of smart reader for visually impaired using raspberry pi. IJAREEIE 5(3), 2055–2063 (2016)Google Scholar
  9. 9.
    Nagaraja, L., et al.: Vision based text recognition using raspberry pi. Int. J. Comput. Appl. 0975, 8887 (2015). National Conference on Power Systems Industrial Automation(NCPSIA 2015)Google Scholar
  10. 10.
    Ye, Z., Yi, C., Tian, Y.: Reading labels of cylinder objects for blind persons. In: 2017 IEEE International Conference on Environment and Electrical Engineering, pp. 1–6 (2017)Google Scholar
  11. 11.
    Rong, X., Yi, C., Yang, X., Tian, Y.: Scene text recognition in multiple frames based on text tracking, pp. 1417–1422Google Scholar
  12. 12.
    Yi, C.,, Tian, Y.: Portable camera-based assistive text and product label reading from hand-held objects for blind persons. In: IEEE/ASME Transactions on Mechatronics Student Member, vol. 19, pp. 1083–4435. IEEE (2014)CrossRefGoogle Scholar
  13. 13.
    Singh, R., Yadav, C.S., Verma, P., Yadav, V.: Optical character recognition (ocr) for printed devnagari script using artificial neural network. Int. J. Comput. Sci. Commun. 1(1), 91–95 (2010)Google Scholar
  14. 14.
    Sabu, A.M., Das, A.S.: A survey on various optical character recognition techniques. In: Proceedings IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018), March 2018Google Scholar
  15. 15.
    Smith, R.: An overview of the tesseract ocr engine. In: Ninth International Conference on Document Analysis and Recognition (2007)Google Scholar
  16. 16.
    The MagPi magazine (1999). Accessed 30 Sept 2010.
  17. 17.
    The World Health Organisation statistics (2018). Accessed 10 Oct 2018.
  18. 18.
    Arrahmaha, A.I., Rahmatika, A., Harisa, S., Zakaria, H., Mengko, R.: Text-to-speech device for patients with low vision. In: 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), November 2015Google Scholar
  19. 19.
    Paajala, I.J., Kernen, N.: Study for acceptance on new navigation assistance by visually impaired people. In: 2015 9th International Symposium on Medical Information and Communication Technology (ISMICT), September 2015Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gayathri Rajendrababu
    • 1
    Email author
  • Rajesh Kannan Megalingam
    • 1
  1. 1.Department of Electronics and Communications EngineeringAmrita Vishwa VidyapeethamAmritapuriIndia

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