Skip to main content
Book cover

International Conference on ISMAC in Computational Vision and Bio-Engineering

ISMAC 2018: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) pp 147–157Cite as

Real-Time Input Text Recognition System for the Aid of Visually Impaired

  • 1391 Accesses

Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB,volume 30)

Abstract

It is estimated that 285 million people globally are visually impaired. A majority of these people live in developing countries and are among the elderly population. Reading is essential in daily life for everyone. Visually impaired persons can read only by use of special scripts specially designed for them such as Braille language. Further, only trained people can read and understand. Since every product does not provide the product information on product cover in Braille, the present work proposes an assistive text reading framework to help visually impaired persons to read texts from various products/objects in their daily lives. The first step in implementation captures the image of the required by extracting frames from real-time video input from the camera. This is followed by preprocessing steps which includes conversion to grey scale and filtering. The text regions are further extracted using MSER followed by canny edge detection. The text regions from the captured image are then extracted and recognized by using Optical Character Recognition software (OCR). The OCR engine Tesseract is used here. This extracts the text of various fonts and then sizes can be recognized individually and then combined to form a word. Further, producing audio output by using Text to Speech module. The result obtained is very much comparable with other existing methods with better time efficiency. The real-time input is taken and passed through the algorithm which applies filters and removes noise then later image is passed through MSER, OCR, Canny edge detection to get the final audio output.

Keywords

  • Maximally stable extremal regions
  • Optical character recognition
  • Canny edge detection
  • Real-time visual aid

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-00665-5_16
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-00665-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   59.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Strotthe T et al (1997) Mobility of blind and elderly people interacting with computers. National Institute for the Blind, report on the MOBIC project. http://www.tiresias.org/reports/mobicf.htm

  2. Real Time Text Detection and Recognition on Hand Held Objects to Assist Blind People. In: 2016 international conference on automatic control and dynamic optimization techniques (ICACDOT), International Institute of Information Technology (I2IT), Pune

    Google Scholar 

  3. Venkateswarlu K, Velaga SM. Text detection on scene images using MSER

    Google Scholar 

  4. Islam MR, Mondal C, Azam MK, Syed A, Islam MJ Text detection and recognition using enhanced MSER detection and a novel OCR technique

    Google Scholar 

  5. Gómez L, Karatzas D. MSER-based real-time text detection and tracking

    Google Scholar 

  6. Kim KI, Jung K, Kim JH (2003) Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans Pattern Anal Mach Intelligence 25(12):1631–1639

    CrossRef  Google Scholar 

  7. Koo HI, Kim DH (2013) Scene text detection via connected component clustering and nontext filtering. IEEE Trans Image Process 22(6):2296–2305

    CrossRef  MathSciNet  MATH  Google Scholar 

  8. Srivastav A, Kumar J (2008) Text detection in scene images using stroke width and nearest-neighbor constraints. In: TENCON IEEE region 10 conference, pp 1–5

    Google Scholar 

  9. Zhou G, Liu Y, Tian Z, Su Y (2011) A new hybrid method to detect text in natural scene. In: 18th IEEE international conference on image processing (ICIP), pp 2605–2608

    Google Scholar 

  10. Gómez L, Karatzas D (2014) MSER-based real-time text detection and tracking. In: 22nd international conference on pattern recognition (ICPR), pp 3110–3115

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, R V College of Engineering, Bengaluru, India

    B. K. RajithKumar, Divya A. Jamakhandi, K. V. Akshatha, Disha B. Hegde & Amisha Singh

  2. Department of Electronics and Instrumentation Engineering, Malnad College of Engineering, Hassan, India

    H. S. Mohana

Authors
  1. B. K. RajithKumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. S. Mohana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Divya A. Jamakhandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. V. Akshatha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Disha B. Hegde
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amisha Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. K. RajithKumar .

Editor information

Editors and Affiliations

  1. SCAD Institute of Technology, Palladam, India

    Durai Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. School of Computer and Security Science, Edith Cowan University, Joondalup, WA, Australia

    Zubair Baig

  4. Wenzhou Medical University, Wenzhou, China

    Fuqian Shi

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

RajithKumar, B.K., Mohana, H.S., Jamakhandi, D.A., Akshatha, K.V., Hegde, D.B., Singh, A. (2019). Real-Time Input Text Recognition System for the Aid of Visually Impaired. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00665-5_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00664-8

  • Online ISBN: 978-3-030-00665-5

  • eBook Packages: EngineeringEngineering (R0)