Fuzzy Single-Stroke Character Recognizer with Various Rectangle Fuzzy Grids

  • Alex Tormási
  • László T. Kóczy
Part of the Studies in Computational Intelligence book series (SCI, volume 530)


In this chapter we introduce the results of a formerly published FUBAR character recognition method with various fuzzy grid parameters. The accuracy and efficiency of the handwritten single-stroke character recognition algorithm with different sized rectangle (N \(\times \) M) fuzzy grids are investigated. The results are compared to other modified FUBAR algorithms and known commercial and academic recognition methods. Possible applications and further extensions are also discussed. This work is the extended and fully detailed version of a previously published abstract.


Fuzzy logic Fuzzy systems Fuzzy grid Single-stroke character recognition 



This chapter was supported by the National Scientific Research Fund Grant OTKA K75711 and OTKA K105529, a Széchenyi István University Main Research Direction Grant and EU grant TÁMOP 421 B, TÁMOP 4.2.2/B-10/1-2010-0010.


  1. 1.
    LaLomia, M.J.: User acceptance of handwritten recognition accuracy. Companion Proceedings CHI ’94, New York, p. 107 (1994)Google Scholar
  2. 2.
    Tormási, A., Botzheim, J.: Single-stroke character recognition with fuzzy method. In: Balas, V.E. et al. (eds.) New Concepts and Applications in Soft Computing SCI, vol. 417, pp. 27–46 (2012)Google Scholar
  3. 3.
    Tormási, A., Kóczy, L.T.: Comparing the efficiency of a fuzzy single-stroke character recognizer with various parameter values. In: Greco, S. et al. (ed.) Proceedings of IPMU, : Part I. CCIS, vol. 297, pp. 260–269 (2012)Google Scholar
  4. 4.
    Tormasi, A., Kóczy, L.T.: Efficiency and accuracy analysis of a fuzzy single-stroke character recognizer with various rectangle fuzzy grids. In: Proceedings of CSCS ’12, Szeged, pp. 54–55 (2012)Google Scholar
  5. 5.
    Tormási, A., Kóczy, L.T.: Improving the accuracy of a fuzzy-based single-stroke character recognizer by antecedent weighting. In: Proceedings of 2nd World Conference on Soft Computing, Baku, pp. 172–178 (2012)Google Scholar
  6. 6.
    Tormási, A., Kóczy, L.T.: Improving the efficiency of a fuzzy-based single-stroke character recognizer with hierarchical rule-base. In: Proceedings of 13th IEEE International Symposium on Computational Intelligence and Informatics, Óbuda, pp. 421–426 (2012)Google Scholar
  7. 7.
    Fleetwood, M.D. et al.: An evaluation of text-entry in palm OS–Graffiti and the virtual keyboard. In: Proceedings of HFES ‘02, Santa Monica, CA, pp. 617–621 (2002)Google Scholar
  8. 8.
    Költringer, T., Grechenig, T.: Comparing the immediate usability of graffiti 2 and virtual keyboard. In: Proceedings of CHI EA ’04, New York, pp. 1175–1178 (2004)Google Scholar
  9. 9.
    Wobbrock, J.O., Wilson, A.D., Li, Y.: Gestures without libraries, toolkits or training: a 1 recognizer for user interface prototypes. In: Proceedings of UIST ’07. ACM Press, New York, pp. 159–168 (2007)Google Scholar
  10. 10.
    Anthony, L., Wobbrock, J.O.: The N multi-stroke recognizer. In: Proceedings of GI’10, Ottawa, pp. 245–253 (2010)Google Scholar
  11. 11.
    Butter, A., Pogue, D.: Piloting Palm: The Inside Story of Palm, Handspring, and the Birth of the Billion-Dollar Handheld Industry. Wiley, New York (2002)Google Scholar
  12. 12.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)Google Scholar
  14. 14.
    Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 7(5), 608–616 (1999)CrossRefGoogle Scholar
  15. 15.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)CrossRefMATHGoogle Scholar
  16. 16.
    Takagi, T. Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15, 116–132 (1985)Google Scholar
  17. 17.
    Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 9(4), 506–515 (2001)CrossRefGoogle Scholar
  18. 18.
    van den Berg, J., Kaymak, U., van den Bergh, W.M.: Fuzzy classification using probability-based rule weighting. In: Proceedings of 11th IEEE International Conference on Fuzzy Systems, Hawaii (2002)Google Scholar
  19. 19.
    Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13(4), 428–435 (2005)CrossRefGoogle Scholar
  20. 20.
    Sugeno, M., Griffin, F.M., Bastian, A.: Fuzzy hierarchical control of an unmanned helicopter. In: Proceedings of IFSA ’93, Seoul, pp. 1262–1265 (1993)Google Scholar
  21. 21.
    Sugeno, M., Park, K.G.: An approach to linguistic instruction based learning. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 1(1), 19–56 (1993)CrossRefMATHGoogle Scholar
  22. 22.
    Kóczy, L.T., Hirota, K.: Approximate inference in hierarchical structured rule bases. In: Proceedings of IFSA ’93, Seoul, pp. 1262–1265 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of AutomationSzéchenyi István UniversityGyörHungary
  2. 2.Department of Telecommunications and Media InformaticsBudapest Universityof Technology and EconomicsBudapestHungary

Personalised recommendations