Skip to main content
Springer Nature Link
Log in

An Efficient Fuzzy Method for Handwritten Character Recognition

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

Abstract

The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Due to its low computational requirement, fuzzy logic is probably the most efficient method available for on-line character recognition. The most tedious task associated with using fuzzy logic for online character recognition is the building of the rule-base that would describe the characters to be recognized. The problem is complicated as different people write the same character in complete different ways. This paper describes a method that can be used to generate a fuzzy value database that describes the characters written by different individuals.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Malaviya, A., Peters, L.: Extracting meaningful handwriting features with fuzzy aggregation method. In: International conference on document analysis and recognition, ICDAR 1995, Montreal, Canada, vol. 3, pp. 841–844 (1995)

    Google Scholar 

  2. Kartalopoulos, S.V.: Understanding Neural Networks and Fuzzy Logic, pp. 167–168. IEEE Press, Los Alamitos (1996)

    MATH  Google Scholar 

  3. Pal, S.K., Majumder, D.K.D.: Fuzzy mathematical approach to pattern recognition, New Delhi. A Halsted Press Book, Wiley & Sons, Chichester (1986)

    MATH  Google Scholar 

  4. Bandara, G.E.M.D.C., Pathirana, S.D., Ranawana, R.M.: A Short Method for On-line Alphanumeric Character Recognition’. In: NAFIPS – FLINT 2002, New Orleans, USA (June, 2002)

    Google Scholar 

  5. Bandara, G.E.M.D.C., Pathirana, S.D., Ranawana, R.M.: Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters. In: 1st Conference on Fuzzy Systems and Knowledge Discovery, Singapore (November, 2002)

    Google Scholar 

  6. Cheung, K., Yeung, D.: Bidirectional Deformable Matching with Application to Handwritten Character Extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8) (August 2002)

    Google Scholar 

  7. Koerich, A.L., Leydier, Y.: A Hybrid Large Vocabulary Word Recognition System using Neural Networks with Hidden Markov Models. IWFHR (2002), http://citeseer.nj.nec.com/koerich02hybrid.html

  8. Lee, S., Kim, Y.: A new type of recurrent neural network for handwritten character recognition. In: hird International Conference on Document Analysis and Recognition, vol. 1 (1995)

    Google Scholar 

  9. Kim, G., Kim, S.: Feature Selection Using Genetic Algorithms for Handwritten Character Recognition. In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computing Laboratory, Oxford University, Oxford, OX1 3QD, United Kingdom

    Romesh Ranawana & Vasile Palade

  2. Faculty of Engineering, University of Peradeniya, Peradeniya, Sri Lanka

    G. E. M. D. C. Bandara

Authors
  1. Romesh Ranawana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vasile Palade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. E. M. D. C. Bandara
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ranawana, R., Palade, V., Bandara, G.E.M.D.C. (2004). An Efficient Fuzzy Method for Handwritten Character Recognition. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_92

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30133-2_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics