Skip to main content
Log in

Improved canny detection algorithm for processing and segmenting text from the images

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Generally, the blind peoples have several visual complications in doing their daily actions. So, the computer vision structure is used to developing the visually weakened life eminence. Unfortunately, the world is not contains any type of previous intermediate or interface. The proposed system is able to retrieve the text contents from the images and processes them with various edges. The anticipated system is used to engender the speech as several text documents. This document results with an effectual processing of text and segmenting of text from the images to support in actual text detection. The anticipated process is executed in the working platform of MATLAB and the consequences are examined by the obtainable processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Santhana Kumar, D., Ashiq Irphan, K.: Speech Recognition Examination System for physically challenged people using android. J. Res. Comput. Sci. Technol. 2(4) (2016)

  2. Cecilio, J., Duarte, K., Furtado, P.: BlindeDroid: an information tracking system for real-time guiding of blind people. J. Procedia Comput. Sci. 52, 113–120 (2015)

    Article  Google Scholar 

  3. Anupriya, A., Vijayalakshmi, S.: An efficient examination system for blinds with real time voice interface. J. Comput. Sci. Mob. Comput. 4(3), 345–348 (2015)

    Google Scholar 

  4. Khan, S.: Voice based online examination for physically challenged. Int. J. Comput. Sci. Inf. Technol. 5(2), 58–61 (2015)

    Google Scholar 

  5. Fook, C.Y., Hariharan, M., Yaacob, S., Adom, A.H.: A review: Malay speech recognition and audio visual speech recognition. In: International Conference on Biomedical Engineering, pp. 479–484, 2012

  6. Saini, P., Kaur, P.: Automatic speech recognition: a review. Int. J. Eng. Trends Technol. 4(2) (2013). http://www.internationaljournalssrg.org

  7. Nereveettil, C.J., Kalamani, M., Valarmathy, S.: Feature selection algorithm for automatic speech recognition based on fuzzy logic. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 3(1) (2014). www.ijareeie.com

  8. Ganesh, S., Kumar, S., Shankar, Raj, S.D., Kartik, R.: A novel voice recognition system for dumb people. J. Theor. Appl. Inf. Technol. 56(2), 296–304 (2013)

    Google Scholar 

  9. Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S.: Scene text detection using graph model built upon maximally stable extremal region. Pattern Recognit. Lett. 34(2), 107–116 (2013)

    Article  Google Scholar 

  10. Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: International Conference on Computer Vision ICCV 2011, vol. 10, pp. 1457–1464

  11. Sumathi, C.P., Santhanam, T., Gayathri Devi, G.: A survey on various approaches of text extraction images. Int. J. Comput. Sci. Eng. Surv. 3, 27–42 (2012)

    Article  Google Scholar 

  12. Yin, X.-C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36, 970–983 (2013)

    Google Scholar 

  13. Zhang, H., Zhao, K., Song, Y.-Z., Guo, J.: Text extraction from natural scene image: a survey. J. Neurocomput. (2013). https://doi.org/10.1016/j.neucom.2013.05.037

    Article  Google Scholar 

  14. Gopala Krishnan, K., Porkodi, C.M., Kanimozhi, K.: Image recognition for visually impaired people by sound. J. Commun. Signal Process. (2013). https://doi.org/10.1109/iccsp.2013.6577195

    Article  Google Scholar 

  15. Naik, A., Patil, K., Patil, V.: E-Blind examination system. J. Innov. Res. Sci. Technol. 1(11), 238–242 (2015)

    Google Scholar 

  16. Ghosalkar, S., Pandey, S., Padhra, S., Apte, T.: Android application on examination using speech technology for blind people. J. Res. Comput. Commun. Technol. 3(3) (2011). www.ijrcct.org

  17. Nandish, M.S., Balaji, C., Shantala, C.P.: An outdoor navigation with voice recognition security application for visually impaired people. J. Eng. Trends Technol. 10(10) (2014). http://www.ijettjournal.org

  18. Harsur, A., Chitra, M.: Voice based navigation system for blind people using ultrasonic sensor. J. Recent Innov. Trends Comput. Commun. 3(6), 4117–4122 (2017)

    Google Scholar 

  19. Vats, A., et al.: Voice operated tool-examination portal for blind persons. J. Comput. Appl. (2016). https://doi.org/10.5120/ijca2016909989

    Article  Google Scholar 

  20. Balakrishnan, N., Shantharajah, S.P.: An effective segmentation pattern using multi-class independent component analysis on high quality color texture images. Res. J. Appl. Sci. Eng. Technol. (2016). https://doi.org/10.19026/rjaset.12.2809

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Arunkumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arunkumar, P., Shantharajah, S.P. & Geetha, M. Improved canny detection algorithm for processing and segmenting text from the images. Cluster Comput 22 (Suppl 3), 7015–7021 (2019). https://doi.org/10.1007/s10586-018-2056-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2056-8

Keywords

Navigation