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A character image restoration method for unconstrained handwritten Chinese character recognition

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Abstract

Despite the success of methods on constrained handwriting databases, recognition of unconstrained handwritten Chinese characters remains a big challenge. One difficulty for recognizing unconstrained handwritting is that some connected strokes are involved or some strokes are omitted. In this paper, a character image restoration method is proposed for unconstrained handwritten Chinese character recognition. In this method, the observed character image is modeled as the combination of the ideal character image with two types of noise images: the omitted stroke noise image and the added stroke noise image. To preserve the original gradient features, restoration is done on the gradient features. The estimated features are then used to discriminate similar characters. To show the effectiveness of the proposed method, we extend some state-of-the-art classifiers based on the estimated features. Experimental results show that the extended classifiers outperform the original state-of-the-art classifiers. This demonstrates that the estimated features are useful for further improving the recognition rate.

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References

  1. 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 

  2. 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 

  3. 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 

  4. 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)

  5. Liu, C.-L.: Handwritten Chinese character recognition: effects of shape normalization and feature extraction. In: Doermann, D., Jaeger, S. (eds.) Arabic and Chinese Handwriting Recognition. Lecture Notes in Computer Science, vol. 4768, pp. 104–128. Springer, Berlin, Heidelberg (2008)

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

  7. 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 

  8. 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 4th International Conference on Document Analysis and Recognition, pp. 511–514 (1997)

  9. 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 

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

  11. Liu, C.-L., Fujisawa, H.: Classification and learning methods for character recognition: advances and remaining problems. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. Studies in Computational Intelligence, vol. 90, pp. 139–161. Springer, Berlin, Heidelberg (2008)

  12. 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 10th International Conference on Document Analysis and Recognition, pp. 1026–1030 (2009)

  13. Yamashita, Y., Higuchi, K., Yamada, Y., Haga, Y.: Classification of handprinted Kanji characters by the structured segment matching method. Pattern Recognit. Lett. 1(5), 475–479 (1983)

    Article  Google Scholar 

  14. Casey, R.G.: Moment normalization of handprinted character. IBM J. Res. Dev. 14, 548–557 (1970)

    Article  MATH  Google Scholar 

  15. 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 

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

    Google Scholar 

  17. 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 

  18. 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 

  19. 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 

  20. 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: International Conference on Wavelet Analysis and Pattern Recognition, pp. 1182–1186 (2007)

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

  22. 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 

  23. 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 

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

  25. Liu, C.-L., Sako, H., Fujisawa, H.: Performance evaluation of pattern classifiers for handwritten character recognition. Int. J. Doc. Anal. Recognit. 4(3), 191–204 (2002)

  26. Suzuki, M., Ohmachi, S., Kato, N., Aso, H., Nemoto, Y.: A discrimination method of similar characters using compound Mahalanobis function. Trans. IEICE Jpn. vJ80-D-II(10), 2752–2760 (1997)

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

  28. Leung, K.C., Leung, C.H.: Recognition of handwritten Chinese characters by critical region analysis. Pattern Recognit. 43(3), 949–961 (2009)

    Article  Google Scholar 

  29. 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 12th International Conference on Frontiers in Handwriting Recognition, pp. 527–532 (2010)

  30. 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: International Conference on Document Analysis and Recognition, pp. 1002–1006 (2011)

  31. Ryu, S., Kim, I.-J.: Discrimination of similar characters using nonlinear normalization based on regional importance measure. Int. J. Doc. Anal. Recognit. 17(1), 79–89 (2014)

    Article  Google Scholar 

  32. Shao, Y.X., Wang, C.H., Xiao, B.H.: Fast self-generation voting for handwritten Chinese character recognition. Int. J. Doc. Anal. Recognit. 16(4), 413–424 (2013)

    Article  Google Scholar 

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

  34. 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)

    Article  MathSciNet  Google Scholar 

  35. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  36. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

  37. Lee, S.-W., Park, J.-S., Tang, Y.: Performance evaluation of nonlinear shape normalization methods for the recognition of large-set handwritten characters. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 402–407 (1993)

  38. Everitt, B.S.: The Analysis of Contingency Tables. Chapman and Hall, London (1977)

    Book  Google Scholar 

  39. Snedecor, G.W., Cochran, W.G.: Statistical Methods, 8th edn. Iowa State University Press, Ames, IA (1989)

    MATH  Google Scholar 

  40. 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)

  41. 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)

    Article  MATH  Google Scholar 

  42. Zhang, X.-Y., Liu, C.-L.: Evaluation of weighted Fisher criteria for large category dimensionality reduction in application to Chinese handwriting recognition. Pattern Recognit. 46(9), 2599–2611 (2013)

    Article  Google Scholar 

  43. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649 (2012)

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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 program of higher-level talents of Inner Mongolia University.

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Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, 235 West College Road, Hohhot, Inner Mongolia, 010021, China

    Yunxue Shao

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

    Chunheng Wang & Baihua Xiao

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  1. Yunxue Shao
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  2. Chunheng Wang
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  3. Baihua Xiao
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Correspondence to Yunxue Shao.

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Shao, Y., Wang, C. & Xiao, B. A character image restoration method for unconstrained handwritten Chinese character recognition. IJDAR 18, 73–86 (2015). https://doi.org/10.1007/s10032-014-0232-9

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