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Rough Sets and Fuzzy Logic Approach for Handwritten Digits and Letters Recognition

  • Marcin MajakEmail author
  • Andrzej Żołnierek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

This paper presents the hybrid approach using fuzzy logic and rough sets used as a pattern recognition framework. Both fuzzy and rough sets have been introduced to deal with vagueness and uncertain data in artificial intelligence applications. In general, fuzzy logic can be related to vagueness, while rough sets deal with indiscernibility. In our work we propose two-stage algorithm. At the first stage, an optimization procedure is applied to reduce the number of features for fuzzy membership functions and to find the optimal granulation for rough sets, respectively. In the second stage, two-step classifier is used. We tested our attempt using Handprinted Forms and Characters Database containing the full page binary images of 3699 handwriting sample forms. For any segmented image classification, a crucial part lies in the proper feature extraction method. In our work, cross corner feature algorithm was used as a main tool.

References

  1. 1.
    Cheng-Lin, L., Kazuki, N., Hiroshi, S., Hiromichi, F.: Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognit. 37, 265–279 (2004)CrossRefzbMATHGoogle Scholar
  2. 2.
    Hossain, M., Amim, M., Hong, Y.: Rapid feature extraction for bangla handwritten digit recognition. In: International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1832–1837 (2011)Google Scholar
  3. 3.
    Impedovo, S., Pirlo, G., Mangini, F.: Handwritten digit recognition by multi-objective optimization of zoning methods. In: 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 675–679 (2012)Google Scholar
  4. 4.
    Majak, M.: Universal segmentation framework for medical imaging using rough sets theory and fuzzy logic clustering. Information Technologies in Biomedicine 3 (2014)Google Scholar
  5. 5.
    Majak, M., Zolnierek, A.: Rough sets approach to the problems of classification. In: Proceedings of International Conference MOSIS X, pp. 109–114 (2010)Google Scholar
  6. 6.
    Nabiha, A., Nadir, F.: New dynamic ensemble of classifiers selection approach based on confusion matrix for arabic handwritten recognition. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 308–313 (2012)Google Scholar
  7. 7.
    Qinghua, H., Zongxia, X.: Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognit. 40, 3509–3521 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Singh, P., Verma, A.: An experimental evaluation of feature selection based classifier ensemble for handwritten numeral recognition. In: International Conference on Electronics and Communication Systems (ICECS), pp. 1–8 (2014)Google Scholar
  9. 9.
    Wang, J., Fang-Chen, C.: An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition. In: IEEE Transactions on Industrial Electronics, pp. 2998–3007 (2012)Google Scholar
  10. 10.
    Wang, Q., Yang, A., Dai, W.: An improved feature extraction method for individual offline handwritten digit recognition. In: 8-th World Congress on Intelligent Control and Automation (WCICA), pp. 6327–6330 (2010)Google Scholar
  11. 11.
    Yuan, H., Wang, P.: Handwritten digits recognition using multiple instance learning. In: IEEE International Conference on Granular Computing (GrC), pp. 408–411 (2013)Google Scholar
  12. 12.
    Zolnierek, A., Majak, M.: Rough sets approach to the classification task with modification of decision rules. In: Proceedings of the 11th WSEAS International Conference on Systems Theory and Scientific Computation, pp. 53–56 (2011)Google Scholar
  13. 13.
    Zolnierek, A., Majak, M.: Hybrid approach using rough sets and fuzzy logic to pattern recognition task. Lecture Notes in Computer Science 8073 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Electronics, Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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