Advertisement

Comparing the Efficiency of a Fuzzy Single-Stroke Character Recognizer with Various Parameter Values

  • Alex Tormási
  • László T. Kóczy
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

In this paper the results of a study on the accuracy of a fuzzy logic-based single-stroke character recognizer are presented by refining various parameter values, such as resolution of the fuzzy grid and the minimum distance between sampled points.

The symbol set is a modified version of Palm’s Graffiti single-stroke alphabet and it contains 26 different symbols. Each symbol is represented by a single fuzzy rule. The rule base was determined by a subset of the collected samples. 99.4% recognition rate has been achieved with the initial rule base, without training.

With the revised parameter values the accuracy is close or even slightly beyond the results of other academic or commercial systems.

Keywords

Single-stroke character recognition fuzzy logic fuzzy grid fuzzy recognizer punishment/reward bacterial evolutionary algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    LaLomia, M.J.: User Acceptance of Handwritten Recognition Accuracy. In: Companion Proc. CHI 1994, New York, p. 107 (1994)Google Scholar
  2. 2.
    Tormási, A., Botzheim, J.: Single-Stroke Character Recognition with Fuzzy Method. In: Balas, V.E., Fodor, J., Varkonyi-Koczy, A., et al. (eds.) New Concepts and Applications in Soft Computing. SCI, vol. 417, pp. 27–46. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Ruspini, E.H.: A new approach to clustering. Information Control 15(1), 22–32 (1969)zbMATHCrossRefGoogle Scholar
  5. 5.
    Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies 7, 1–13 (1975)zbMATHCrossRefGoogle Scholar
  6. 6.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics SMC-15, 116–132 (1985)Google Scholar
  7. 7.
    Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)Google Scholar
  8. 8.
    Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Transactions on Fuzzy Systems 7(5), 608–616 (1999)CrossRefGoogle Scholar
  9. 9.
    Fleetwood, M.D., et al.: An Evaluation Of Text-Entry In Palm Os – Graffiti And The Virtual Keyboard. In: Proc. HFES 2002, Santa Monica, CA, pp. 617–621 (2002)Google Scholar
  10. 10.
    Költringer, T., Grechenig, T.: Comparing the Immediate Usability of Graffiti 2 and Virtual Keyboard. In: Proc. CHI EA 2004, New York, pp. 1175–1178 (2004)Google Scholar
  11. 11.
    Wobbrock, J.O., Wilson, A.D., Li, Y.: Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes. In: Proc. UIST 2007, pp. 159–168. ACM Press, New York (2007)CrossRefGoogle Scholar
  12. 12.
    Anthony, L., Wobbrock, J.O.: The $N Multi-Stroke Recognizer. In: Proc. GI 2010, Ottawa, pp. 245–253 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alex Tormási
    • 1
  • László T. Kóczy
    • 1
    • 2
  1. 1.Department of AutomationSzéchenyi István UniversityGyőrHungary
  2. 2.Department of Telecommunications and Media InformaticsBudapest University of Technology and EconomicsBudapestHungary

Personalised recommendations