Skip to main content
SpringerLink
Log in

Fast self-generation voting for handwritten Chinese character recognition

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

In this paper, a fast self-generation voting method is proposed for further improving the performance in handwritten Chinese character recognition. In this method, firstly, a set of samples are generated by the proposed fast self-generation method, and then these samples are classified by the baseline classifier, and the final recognition result is determined by voting from these classification results. Two methods that are normalization-cooperated feature extraction strategy and an approximated line density are used for speeding up the self-generation method. We evaluate the proposed method on the CASIA and CASIA-HWDB1.1 databases. High recognition rate of 98.84 % on the CASIA database and 91.17 % on the CASIA-HWDB1.1 database are obtained. These results demonstrate that the proposed method outperforms the state-of-the-art methods and is useful for practical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Tsukumo, J., Tanaka, H.: Classification of handprinted Chinese characters using nonlinear normalization and correlation methods. In: Proceedings of the International Conference on, Pattern Recognition, pp. 168–171 (1988)

  2. Yamada, H., Yamamoto, K., Saito, T.: A nonlinear normalization method for handprinted Kanji character recognition-line density equalization. Pattern Recognit. 23(9), 1023–1029 (1990)

    Article  Google Scholar 

  3. Horiuchi, T., Haruki, R., Yamada, H., Yamamoto, K.: Two-dimensional extension of nonlinear normalization method using line density for character recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 511–514 (1997)

  4. Liu, C.-L., Sako, H., Fujisawa, H.: Handwritten Chinese character recognition: alternatives to nonlinear normalization. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 524–528 (2003)

  5. Liu, C.-L., Marukawa, K.: Pseudo two dimensional shape normalization methods for handwritten Chinese character recognition. Pattern Recognit. 38(12), 2242–2255 (2005)

    Article  Google Scholar 

  6. Trier, O.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-a survey. Pattern Recognit. 29(4), 641–662 (1996)

    Article  Google Scholar 

  7. Yasuda, M., Fujisawa, H.: An improvement of correlation method for character recognition. Trans. IEICE Jpn. J62–D(3), 217–224 (1979)

    Google Scholar 

  8. Kimura, F., Takashina, K., Tsuruoka, S., Miyake, Y.: Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 149–153 (1987)

    Article  Google Scholar 

  9. Srikantan, G., Lam, S.W., Srihari, S.N.: Gradient-based contour encoder for character recognition. Pattern Recognit. 29(7), 1147–1160 (1996)

    Article  Google Scholar 

  10. Shi, M., Fujisawa, Y., Wakabayashi, T., Kimura, F.: Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recognit. 35(10), 2051–2059 (2002)

    Article  MATH  Google Scholar 

  11. Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognit. 37(2), 265–279 (2004)

    Article  MATH  Google Scholar 

  12. Ding, K., Liu, Z.B., Jin, L.W., Zhu, X.H.: A comparative study of gabor feature and gradient feature for handwritten chinese character recognition. in: Proceedings of the International Conference on Wavelet Analysis and, Pattern Recognition, pp. 1182–1186 (2007)

  13. Kimura, F., Wakabayashi, T., Tsuruoka, S., Miyake, Y.: Improvement of handwritten Japanese character recognition using weighted direction code histogram. Pattern Recognit. 30(8), 1329–1337 (1997)

    Article  Google Scholar 

  14. Kato, N., Suzuki, M., Omachi, S., Aso, H., Nemoto, Y.: A handwritten character recognition system using directional element feature and asymmetric mahalanobis distance. IEEE Trans. Pattern Anal. Mach. Intell. 21(3), 258–262 (1999)

    Article  Google Scholar 

  15. Kawatani, T.: Handwritten Kanji recognition with determinant normalized quadratic discriminant function. In: Proceedings of the International Conference on, Pattern Recognition, pp. 343–346 (2000)

  16. Liu, C.-L., Sako, H., Fujisawa, H.: Performance evaluation of pattern classifiers for handwritten character recognition. Int. J. Document Anal. Recognit. 4, 191–204 (2002)

    Article  Google Scholar 

  17. Suzuki, M., Ohmachi, S., Kato, N., Aso, H., Nemoto, Y.: A discrimination method of similar characters using compound Mahalanobis function. Trans. IEICE Jpn J80–D–II(10), 2752–2760 (1997)

    Google Scholar 

  18. Gao, T.-F., Liu C.-L.: LDA-based compound distance for handwritten Chinese character recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 904–908 (2007)

  19. Gao, T.-F., Liu, C.-L.: High accuracy handwritten Chinese character recognition using LDA-based compound distances. Pattern Recognit. 41(11), 3442–3451 (2008)

    Article  MATH  Google Scholar 

  20. Leung, K.C., Leung, C.H.: Recognition of handwritten Chinese characters by critical region analysis. Pattern Recognit. 43(3), 949–961 (2010)

    Article  MATH  Google Scholar 

  21. Xu, B., Huang, K.Z., Liu, C.-L.: Similar handwritten Chinese characters recognition by critical region selection based on average symmetric uncertainty. In: Proceedings of the International Conference on Frontiers in Handwriting Recognition, pp. 527–532 (2010)

  22. Shao, Y.X., Wang, C.H., Xiao, B.H., Zhang, R.G., Zhang, Y.: Multiple instance learning based method for similar handwritten Chinese characters discrimination. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1002–1006 (2011)

  23. Rahman, A.F.R., Fairhurst, M.C.: Multiple classifier decision combination strategies for character recognition: a review. Int. J. Document Anal. Recognit. 5(4), 166–194 (2003)

    Article  Google Scholar 

  24. Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)

    Article  Google Scholar 

  25. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  26. Beymer, D., Poggio, T.: Face recognition from one example view. In: Proceedings of the International Conference on Computer Vision, pp. 500–507 (1995)

  27. Ha, T., Bunke, H.: On line handwritten numeral recognition by perturbation method. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 535–539 (1997)

    Article  Google Scholar 

  28. Dahmen, J., Keysers, D., Ney, H.: Combined classification of handwritten digits using the virtual test sample method. Multiple Classifier Systems, pp. 99–108 (2001)

  29. Hamanaka, M., Yamada, K., Tsukumo, J.: Normalization-cooperated feature extraction method for handprinted Kanji character recognition. In: Proceedings of the International Workshop on Frontiers of Handwriting Recognition, pp. 343–348 (1993)

  30. Leung, C.-H., Cheung, Y.-S., Chan, K.-P.: A distortion model for Chinese character generation. In: Proceedings of the International Conference on Systems, Man and, Cybernetics, pp. 38–41 (1985)

  31. Leung, K.C., Leung, C.H.: Recognition of handwritten Chinese characters by combining regularization, fisher’s discriminant and distorted sample generation. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1026–1030 (2009)

  32. Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: CASIA online and offline Chinese handwriting databases. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 37–41 (2011)

  33. Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recognit. 46(1), 155–162 (2013)

    Google Scholar 

  34. Liu, C.-L.: Normalization-cooperated gradient feature extraction for handwritten character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1465–1469 (2007)

    Article  Google Scholar 

  35. Liu, C.-L., Mine, R., Koga, M.: Building compact classifier for large character set recognition using discriminative feature extraction. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 846–850 (2005)

  36. Jin, X.-B., Liu, C.-L., Hou, X.: Regularized margin-based conditional log-likelihood loss for prototype learning. Pattern Recognit. 43(7), 2428–2438 (2010)

    Google Scholar 

  37. Liu, C.-L.: Handwritten Chinese character recognition: effects of shape normalization and feature extraction. In: Arabic and Chinese Handwriting Recognition, pp. 104–128 (2008)

  38. Liu, C.-L.: High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction. In: Proceedings of the International Conference on, Pattern Recognition, pp. 942–945 (2006)

Download references

Acknowledgments

We would like to express our sincere appreciation to the anonymous reviewers for their insightful comments, which have greatly aided us in improving the quality of the paper. This work was supported by National Natural Science Foundation of China (61172103,60933010,60835001).

Author information

Authors and Affiliations

  1. Institute of Automation Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190 , Beijing, China

    Yunxue Shao, Chunheng Wang & Baihua Xiao

Authors
  1. Yunxue Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baihua Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunxue Shao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shao, Y., Wang, C. & Xiao, B. Fast self-generation voting for handwritten Chinese character recognition. IJDAR 16, 413–424 (2013). https://doi.org/10.1007/s10032-012-0194-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-012-0194-8

Keywords

Search

Navigation