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Handwriting Recognition Accuracy Improvement by Author Identification

  • Jerzy Sas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

In this paper, two level handwriting recognition concept is presented, where writer identification is used in order to increase handwriting recognition accuracy. On the upper level, author identification is performed. Lower level consists of a classifiers set trained on samples coming from individual writers. Recognition from upper level is used on the lower level for selecting or combining classifiers trained for identified writers. The feature set used on the upper level contains directional features as well as the features characteristic for general writing style as line spacing, tendency to line skewing and proportions of text line elements, which are usually lost in typical process of handwritten text normalization. The proposed method can be used in applications, where texts subject to recognizing come form relatively small set of known writers.

Keywords

Word Recognition Recognition Accuracy Character Recognition Multi Layer Perceptron Handwriting Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Liu, C., Koga, M., Sako, H., Fujisawa, H.: Aspect ratio adaptive normalization for handwritten character recognition. In: Tan, T., Shi, Y., Gao, W. (eds.) ICMI 2000. LNCS, vol. 1948, pp. 418–425. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Said, H.E.S., Tan, T.N., Baker, K.D.: Personal identification based on handwriting. Pattern Recognition 33, 149–160 (2000)CrossRefGoogle Scholar
  3. 3.
    Cha, S.H., Srihari, S.N.: Multiple Feature Integration for Writer Integration. In: Proc. of Seventh International Workshop on Frontiers in Handwriting Recognition, pp. 333–342 (2000)Google Scholar
  4. 4.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley, Chichester (2001)MATHGoogle Scholar
  5. 5.
    Kuncheva, L.: Combining classifiers: soft computing solutions. In: Pal, S., Pal, A. (eds.) Pattern Recognition: from Classical to Modern Approaches, pp. 427–451. World Scientific, Singapore (2001)CrossRefGoogle Scholar
  6. 6.
    Verma, B., Gader, P., Chen, W.: Fusion of multiple handwritten word recognition techniques. Pattern recognition Letters 22, 991–998 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Liu, C., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn 36, 2271–2285 (2003)MATHCrossRefGoogle Scholar
  8. 8.
    Schomaker, L., Bulacu, M., van Erp, M.: Sparse-parametric writer identification using heterogeneous feature groups. In: Proc. of Int. Conf. on Image Processing ICIP, vol. 1, pp. 545–548 (2003)Google Scholar
  9. 9.
    Bulacu, M., Schomaker, L., Vuurpijl, V.: Writer identification using edge-based directional features. In: Proc. of Seventh International Conference on Document Analysis and Recognition, vol. 2, pp. 937–941 (2003)Google Scholar
  10. 10.
    Gunes, V., Menard, M., Loonis, P.: Combination, cooperation and selection of classifiers: a state of art. Int. Journal of Pattern Recognition and Artificial Intelligence 17(8), 1303–1324 (2003)CrossRefGoogle Scholar
  11. 11.
    Rahman, A.F.R., Fairhurst, M.C.: Multiple classifier decision combination strategies for character recognition: a review. Int. Journal on Document Analysis and Recognition 5, 166–194 (2003)CrossRefGoogle Scholar
  12. 12.
    Schlapbach, A., Bunke, H.: Off-line handwriting identification using HMM based recognizers. In: Proc. 17th Int. Conf. on Pattern Recognition, vol. 2, pp. 654–658 (2004)Google Scholar
  13. 13.
    Schomaker, L., Bulacu, M., Franke, K.: Automatic writer identification using fragmented connected-component contours. In: Proc. of 9th IWFHR, pp. 185–190. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  14. 14.
    Sas, J., Luzyna, M.: Combining character classifier using member classifiers assessment. In: Proc. of 5th Int. Conf. on Intelligent Systems Design and Applications, ISDA 2005, pp. 400–405. IEEE Press, Los Alamitos (2005)Google Scholar
  15. 15.
    Kurzynski, M., Sas, J.: Combining character level classifier and probabilistic lexicons in handprinted word recognition - comparative analysis of methods. In: Proc. XI Int. Conference on Computer Analysis and Image Processing. LNCS, pp. 330–337. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jerzy Sas
    • 1
  1. 1.Institute of Applied InformaticsWroclaw University of TechnologyWroclawPoland

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