Abstract
The continuous increase in demand to discover robust and low cost optical character recognition (OCR) systems has prompted researchers to look for rigorous methods of character recognition. In the past OCR systems have been built through traditional pattern recognition and machine learning approaches. There has always been a quest to develop best OCR products which satisfy the user’s needs. Since past few decades soft computing techniques have come up as a promising candidate for the development of cost effective OCR systems. Some important soft computing techniques for optical character recognition (OCR) systems are presented in this chapter. They are hough transform for fuzzy feature extraction, genetic algorithms (GA) for feature selection, fuzzy multilayer perceptron (FMLP), rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM), fuzzy rough versions of support vector machine (FRSVM), hierarchical fuzzy bidirectional recurrent neural networks (HFBRNN) and fuzzy markov random fields (FMRF). These techniques are used for developing OCR systems for different languages viz English, French, German, Latin, Hindi and Gujrati languages. The soft computing methods are used in the different steps of OCR systems discussed in Chap. 2. A comprehensive assessment of these methods is performed in Chaps. 4–9 for the stated languages. A thorough understanding of this chapter will help the readers to appreciate the reading material presented in the abovementioned chapters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
Chaudhuri, A., Fuzzy Rough Support Vector Machine for Data Classification, International Journal of Fuzzy System Applications, 5(2), pp 26–53, 2016.
Chaudhuri, A., Modified Fuzzy Support Vector Machine for Credit Approval Classification, AI Communications, 27(2), pp 189–211, 2014.
Chaudhuri, A., De, Fuzzy Support Vector Machine for Bankruptcy Prediction, Applied Soft Computing, 11(2), pp 2472–2486, 2011.
Chaudhuri, A., Applications of Support Vector Machines in Engineering and Science, Technical Report, Birla Institute of Technology Mesra, Patna Campus, India, 2011.
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.
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.
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.
Chaudhuri, A., Studies in Applications of Soft Computing to some Optimization Problems, PhD Thesis, Netaji Subhas Open University, Kolkata, India, 2010.
Cheriet, M., Kharma, N., Liu, C. L., Suen, C. Y., Character Recognition Systems: A Guide for Students and Practitioners, John Wiley and Sons, 2007.
De, R. K., Pal, N. R., Pal, S. K. Feature Analysis: Neural Network and Fuzzy Set Theoretic Approaches, Pattern Recognition, 30(10), pp 1579–1590, 1997.
Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Reading, Mass, Addison Wesley, 1989.
Haykin, S., Neural Networks and Learning Machines, 3rd Edition, Prentice Hall, 2008.
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.
Klir, G. J., Yuan, B., Fuzzy Sets and Fuzzy Logic, Prentice Hall, New Jersey, 1995.
Mitchell, M., An Introduction to Genetic Algorithms, MIT Press, 1998.
Pal, S. K., Mitra, S., Mitra, P., Rough-Fuzzy Multilayer Perception: Modular Evolution, Rule Generation and Evaluation, IEEE Transactions on Knowledge and Data Engineering, 15(1), pp 14–25, 2003.
Pal, S. K., Soft Computing Pattern Recognition: Principles, Integrations and Data Mining, In T. Tassano et al. (Editors), New Frontiers in Artificial Intelligence, Lecture Notes in Computer Science, Springer Verlag, Berlin, LNCS 2253, pp 261–271, 2001.
Polkowski, L, Rough Sets – Mathematical Foundations, Advances in Intelligent and Soft Computing, Springer Verlag, 2002.
Pratihar, D. K., Soft Computing, Alpha Science International Limited, 2007.
Yen, J., Langari, R., Fuzzy Logic: Intelligence, Control and Information, Pearson Education, 2005.
Young, T. Y., Fu, K. S., Handbook of Pattern Recognition and Image Processing, Academic Press, 1986.
Zadeh, L. A., Fuzzy Logic, Neural Networks and Soft Computing, Communications of the ACM, 37(3), pp 77–84, 1994.
Zadeh, L. A., Fuzzy Sets as a Basis for a Theory of Possibility, Fuzzy Sets and Systems, 1(1), pp 3–28, 1978.
Zadeh, L. A., Fuzzy Sets, Information and Control, 8(3), pp 338–353, 1965.
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.
Zimmermann, H. J., Fuzzy Set Theory and its Applications, 4th Edition, Kluwer Academic Publishers, Boston, 2001.
Author information
Authors and Affiliations
Corresponding author
Rights 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). Soft Computing Techniques for Optical Character Recognition Systems. 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_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-50252-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50251-9
Online ISBN: 978-3-319-50252-6
eBook Packages: EngineeringEngineering (R0)