Skip to main content
SpringerLink
Log in

Writer adaptation via deeply learned features for online Chinese handwriting recognition

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

Abstract

This paper proposes a novel framework of writer adaptation based on deeply learned features for online handwritten Chinese character recognition. Our motivation is to further boost the state-of-the-art deep learning-based recognizer by using writer adaptation techniques. First, to perform an effective and flexible writer adaptation, we propose a tandem architecture design for the feature extraction and classification. Specifically, a deep neural network (DNN) or convolutional neural network (CNN) is adopted to extract the deeply learned features which are used to build a discriminatively trained prototype-based classifier initialized by Linde–Buzo–Gray clustering techniques. In this way, the feature extractor can fully utilize the useful information of a DNN or CNN. Meanwhile, the prototype-based classifier could be designed more compact and efficient as a practical solution. Second, the writer adaption is performed via a linear transformation of the deeply learned features which is optimized with a sample separation margin-based minimum classification error criterion. Furthermore, we improve the generalization capability of the previously proposed discriminative linear regression approach for writer adaptation by using the linear interpolation of two transformations and adaptation data perturbation. The experiments on the tasks of both the CASIA-OLHWDB benchmark and an in-house corpus with a vocabulary of 20,936 characters demonstrate the effectiveness of our proposed approach.

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

Similar content being viewed by others

References

  1. Liu, C.-L., Sako, H., Fujisawa, H.: Discriminative learning quadratic discriminant function for handwriting recognition. IEEE Trans. Neural Netw. 15(2), 430–444 (2004)

    Article  Google Scholar 

  2. Du, J., Huo, Q.: Designing compact classifiers for rotation-free recognition of large vocabulary online handwritten Chinese characters. In: Proceedings of ICASSP-2012, pp. 1721–1724 (2012)

  3. Matić, N., Guyon, I., Denker, J., Vapnik, V.: Writer adaptation for on-line handwritten character recognition. In: Proceedings of ICDAR-1993, pp. 187–191 (1993)

  4. Platt, J.C., Matić, N.P.: A constructive RBF network for writer adaptation. In: Proceedings of NIPS-1997 (1997)

  5. Brakensiek, A., Kosmala, A., Rigoll, G.: Comparing adaptation techniques for on-line handwriting recognition. In: Proceedings of ICDAR-2001, pp. 486–490 (2001)

  6. Vuori, V., Korkeakoulu, T.: Adaptive methods for online recognition of isolated handwritten characters. PhD thesis, Helsinki University of Technology (2002)

  7. Kienzle, W., Chellapilla, K.: Personalized handwriting recognition via biased regularization. In: Proceedings of ICML-2006 (2006)

  8. Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)

    Article  Google Scholar 

  9. He, T., Huo, Q.: A study of a new misclassification measure for minimum classification error training of prototype-based pattern classifiers. In: Proceedings of ICPR-2008 (2008)

  10. Wang, Y.-Q., Huo, Q.: A study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. In: Proceedings of ICPR-2010, pp. 1872–1875 (2010)

  11. Feng, Z.-D., Huo, Q.: Confidence guided progressive search and fast match techniques for high performance Chinese/English OCR. In: Proceedings of ICPR-2002, vol. III, pp. 89–92 (2002)

  12. Zhang, X.-Y., Liu, C.-L.: Style transfer matrix learning for writer adaptation. In: Proceedings of CVPR-2011, pp. 393–400 (2011)

  13. Zhang, X.-Y., Liu, C.-L.: Writer adaptation with style transfer mapping. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1773–1787 (2013)

    Article  Google Scholar 

  14. Du, J., Huo, Q.: A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR. Pattern Recognit. 46(8), 2313–2322 (2013)

    Article  Google Scholar 

  15. Du, J., Hu, J.-S., Zhu, B., Wei, S., Dai, L.-R.: Writer adaptation using bottleneck features and discriminative linear regression for online handwritten Chinese character recognition. In: Proceedings of ICFHR-2014 (2014)

  16. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  17. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Li, F.-F.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  18. Yann, L., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 11, 2278–2324 (1986)

    Google Scholar 

  19. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: Proceedings of ICDAR-2011 (2011)

  20. Yin, F., Wang, Q.-F., Zhang, X.-Y., Liu, C.-L.: ICDAR 2013 Chinese handwriting recognition competition. In: Proceedings of ICDAR-2013, pp. 1464–1470 (2013)

  21. Graham, B.: Sparse arrays of signatures for online character recognition. Technical Report, University of Warwick (2013)

  22. Ciresan, D.C., Schmidhuber, J.: Multi-column deep neural networks for offline handwritten Chinese character classification. arXiv:1309.0261 (2013)

  23. Wu, C., Fan, W., He, Y., Sun, J., Naoi, S.: Handwritten character recognition by alternately trained relaxation convolutional neural network. In: Proceedings of ICFHR-2014 (2014)

  24. Du, J., Zhai, J.-F., Hu, J.-S., Zhu, B., Wei, S., Dai, L.-R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: Proceedings of ICDAR-2015 (2015)

  25. Du, J., Hu, J.-S., Zhu, B., Wei, S., Dai, L.-R.: A study of designing compact classifiers using deep neural networks for online handwritten Chinese character recognition. In: Proceedings of ICPR-2014 (2014)

  26. 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)

  27. Bai, Z.-L., Huo, Q.: A study on the use of 8-directional features for online handwritten Chinese character recognition. In: Proceedings of ICDAR-2005, pp. 262–266 (2005)

  28. Grézl, F., Karafiát, M., Kontár, S., Ćernocký, J.: Probabilistic and bottle-neck features for LVCSR of meetings. In: Proceedings of ICASSP-2007, pp. 757–761 (2007)

  29. Yu, D., Seltzer, M. L.: Improved bottleneck features using pretrained deep neural networks. In: Proceedings of INTERSPEECH-2011, pp. 237–240 (2011)

  30. Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  31. Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of IJCAI-2011, pp. 1237–1242 (2011)

  32. Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition In: Proceedings of International Conference on Artificial Neural Networks (2010)

  33. Lin, M., Chen, Q., Yan, S.-C.: Network in network. arXiv:1312.4400v3 (2014)

  34. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification In: Proceedings of CVPR-2012, pp. 3643–3649 (2012)

  35. Yin, F., Zhou, M.-K., Wang, Q.-F., Liu, C.-L.: Style consistent perturbation for handwritten Chinese character recognition In: Proceedings of ICDAR-2013, pp. 1051–1055 (2013)

  36. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks In: Proceedings of AISTATS-2010, pp. 249–256 (2010)

  37. http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/graham/deepcnet.zip

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61671422.

Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, People’s Republic of China

    Jun Du & Jian-Fang Zhai

  2. iFlytek Research, Hefei, Anhui, People’s Republic of China

    Jin-Shui Hu

Authors
  1. Jun Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian-Fang Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin-Shui Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Du.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, J., Zhai, JF. & Hu, JS. Writer adaptation via deeply learned features for online Chinese handwriting recognition. IJDAR 20, 69–78 (2017). https://doi.org/10.1007/s10032-017-0280-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-017-0280-z

Keywords

Search

Navigation