Skip to main content

Introduction

  • Chapter
  • First Online:

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 352))

Abstract

Optical character recognition (OCR) is one of the most popular areas of research in pattern recognition [3, 25] since past few decades. It is an actively studied topic in industry and academia [8, 15, 18, 24] because of its immense application potential. OCR was initially studied in early 1930s [23].

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Berg, J. Van Den, Bergh, W. M. Van Den, Kaymak, U., Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning, ERIM Report Series Research in Management, 2001.

    Google Scholar 

  2. Buckley, J. J., Fuzzy Probability and Statistics, Studies in Fuzziness and Soft Computing, Springer Verlag, 2006.

    Google Scholar 

  3. Bunke, H., Wang, P. S. P. (Editors), Handbook of Character Recognition and Document Image Analysis, World Scientific, 1997.

    Google Scholar 

  4. Chaudhuri, A., Ghosh, S. K., Sentiment Analysis of Customer Reviews Using Robust Hierarchical Bidirectional Recurrent Neural Network, Book Chapter: Artificial Intelligence Perspectives in Intelligent Systems, Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova, Petr Silhavy, Zdenka Prokopova, (Editors), Advances in Intelligent Systems and Computing, Springer International Publishing, Switzerland, Volume 464, pp 249–261, 2016.

    Google Scholar 

  5. Chaudhuri, A., Fuzzy Rough Support Vector Machine for Data Classification, International Journal of Fuzzy System Applications, 5(2), pp 26–53, 2016.

    Google Scholar 

  6. Chaudhuri, A., Modified Fuzzy Support Vector Machine for Credit Approval Classification, AI Communications, 27(2), pp 189–211, 2014.

    Google Scholar 

  7. Chaudhuri, A., De, Fuzzy Support Vector Machine for Bankruptcy Prediction, Applied Soft Computing, 11(2), pp 2472–2486, 2011.

    Google Scholar 

  8. Chaudhuri, A., Some Experiments on Optical Character Recognition Systems for different Languages using Soft Computing Techniques, Technical Report, Birla Institute of Technology Mesra, Patna Campus, India, 2010.

    Google Scholar 

  9. Chaudhuri, A., De, K., Job Scheduling using Rough Fuzzy Multi-Layer Perception Networks, Journal of Artificial Intelligence: Theory and Applications, 1(1), pp 4–19, 2010.

    Google Scholar 

  10. Chaudhuri, A., De, K., Chatterjee, D., Discovering Stock Price Prediction Rules of Bombay Stock Exchange using Rough Fuzzy Multi-Layer Perception Networks, Book Chapter: Forecasting Financial Markets in India, Rudra P. Pradhan, Indian Institute of Technology Kharagpur, (Editor), Allied Publishers, India, pp 69–96, 2009.

    Google Scholar 

  11. Haykin, S., Neural Networks and Learning Machines, 3rd Edition, Prentice Hall, 2008.

    Google Scholar 

  12. Jang, J. S. R., Sun, C. T., Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.

    Google Scholar 

  13. Kosko, B., Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall of India, 2008.

    Google Scholar 

  14. Kreyszig, E., Advanced Engineering Mathematics, 10th Edition, Wiley International Press, 2010.

    Google Scholar 

  15. Leondes, C. T., Image Processing and Pattern Recognition, 1st Edition, Elsevier, 1997.

    Google Scholar 

  16. Liu, H, Motoda, H., Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic, 1998.

    Google Scholar 

  17. Mitchell, M., An Introduction to Genetic Algorithms, MIT Press, 1998.

    Google Scholar 

  18. Mollah, A. F., Majumder, N., Basu, S., Nasipuri, M., Design of an Optical Character Recognition System for Camera based Handheld Devices, International Journal of Computer Science Issues, 8 (4), pp 283–289, 2011.

    Google Scholar 

  19. Padhy, N. P., Simon, S. P., Soft Computing: With MATLAB Programming, Oxford University Press, 2015.

    Google Scholar 

  20. Pal, S. K, Mitra, S., Mitra, P., Rough Fuzzy MLP: Modular Evolution, Rule Generation and Evaluation, IEEE Transactions on Knowledge and Data Engineering, 15 (1), pp 14–25, 2003.

    Google Scholar 

  21. Polkowski, L, Rough Sets – Mathematical Foundations, Advances in Intelligent and Soft Computing, Springer Verlag, 2002.

    Google Scholar 

  22. Pratihar, D. K., Soft Computing, Alpha Science International Limited, 2007.

    Google Scholar 

  23. Rice, S. V., Nagy, G., Nartker, T. A., Optical Character Recognition: An Illustrated Guide to the Frontier, The Springer International Series in Engineering and Computer Science, Springer US, 1999.

    Google Scholar 

  24. Yamasaki, I., Quantitative Evaluation of Print Quality for Optical Character Recognition Systems, IEEE Transactions on Systems, Man and Cybernetics, 8 (5), pp 371–381, 1978.

    Google Scholar 

  25. Yu, F. T. S., Jutamulia, S. (Editors), Optical Pattern Recognition, Cambridge University Press, 1998.

    Google Scholar 

  26. Zadeh, L. A., Fuzzy Logic, Neural Networks and Soft Computing, Communications of the ACM, 37(3), pp 77–84, 1994.

    Google Scholar 

  27. Zadeh, L. A., Fuzzy Sets, Information and Control, 8(3), pp 338–353, 1965.

    Google Scholar 

  28. Zeng, J., Liu, Z. Q., Type-2 Fuzzy Markov Random Fields and their Application to Handwritten Chinese Character Recognition, IEEE Transactions on Fuzzy Systems, 16(3), pp 747–760, 2008.

    Google Scholar 

  29. Zill, D. G., Wright, W. S., Advanced Engineering Mathematics, 4th Edition, Jones and Bartlett Private Limited, 2011.

    Google Scholar 

  30. Zimmermann, H. J., Fuzzy Set Theory and its Applications, 4th Edition, Kluwer Academic Publishers, Boston, 2001.

    Google Scholar 

  31. https://tev-static.fbk.eu/OCR/ResearchProjects.html.

  32. https://office.live.com/start/Excel.aspx.

  33. http://in.mathworks.com/help/vision/optical-character-recognition-ocr.html.

  34. http://in.mathworks.com/products/image/.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arindam Chaudhuri .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Chaudhuri, A., Mandaviya, K., Badelia, P., Ghosh, S.K. (2017). Introduction. In: Optical Character Recognition Systems for Different Languages with Soft Computing. Studies in Fuzziness and Soft Computing, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-319-50252-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50252-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50251-9

  • Online ISBN: 978-3-319-50252-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics