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Improving the Accuracy of a Fuzzy-Based Single-Stroke Character Recognizer by Antecedent Weighting

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Recent Developments and New Directions in Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 317))

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Abstract

In this chapter we present an improved version of the fuzzy based single-stroke character recognizer introduced in previous works. The modified recognition method is able to reach higher accuracy in the character recognition without any significant effect on the computational complexity of the algorithm. Different fuzzy rule and antecedent weighting techniques were successfully used to improve the efficiency of fuzzy systems especially in classification problems. The altered recognizer reached 99.49 % average recognition rate with 26 different single-stroke symbols (based on Palm’s Graffiti alphabet) without learning user-specific parameters or modifying the rule-base. The new algorithm has the same computational complexity as the original system does.

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.

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Authors and Affiliations

  1. Department of Automation, Széchenyi István University, Győr, 9026, Hungary

    A. Tormási & L. T. Kóczy

Authors
  1. A. Tormási
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  2. L. T. Kóczy
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Corresponding author

Correspondence to A. Tormási .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Minister of Communications and Information Technologies of the Republic of Azerbaijan, Baku, Azerbaijan

    Ali M. Abbasov

  3. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  4. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  5. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

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Tormási, A., Kóczy, L.T. (2014). Improving the Accuracy of a Fuzzy-Based Single-Stroke Character Recognizer by Antecedent Weighting. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Directions in Soft Computing. Studies in Fuzziness and Soft Computing, vol 317. Springer, Cham. https://doi.org/10.1007/978-3-319-06323-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-06323-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06322-5

  • Online ISBN: 978-3-319-06323-2

  • eBook Packages: EngineeringEngineering (R0)

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