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An Algorithm of Bidirectional RNN for Offline Handwritten Chinese Text Recognition

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Handwritten Chinese text recognition characters is a challenging problem as it involves a imbalanced training data, and the samples are very different even in same character. In this paper, we propose a novel algorithm based on the bidirectional Recurrent Neural Network (BiRNN) to recognize the characters in the text regions. We solve the problems with pre-processing and improved CNN network. In addition, we utilize RNN to analyze the correlation between characters. Compared with previous works, the algorithm has three distinctive properties: (1) It can predict characters by context analyzing from forward and backward. (2) It solve the problem of sample imbalance effectively. (3) The convergence rate of training has increased. Moreover, the proposed algorithm has achieved good results in recognition.

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References

  1. Luo, X.-L., Huang, S.-F.: Image character recognition technology and its application research based on the integration of multiple classifiers. Software 36(3), 98–102 (2015)

    Google Scholar 

  2. Quan, Z., Lin, J.: Text-independent writer identification method based on Chinese handwriting of small samples. J. East China Univ. Sci. Technol. 44(6), 882–886 (2018)

    Google Scholar 

  3. Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: End-to-end text recognition with convolutional neural networks. In: ICPR, pp. 6–7 (2012)

    Google Scholar 

  4. Liu, W., Li, J.: A method for off-line handwritten Chinese character recognition based on hidden Markov model. CCSSTA 11, 774–777 (2009)

    Google Scholar 

  5. Liu, C.-L., Marukawa, K.: Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition. Pattern Recogn. 32(12), 2242–2255 (2005)

    Article  Google Scholar 

  6. Yan, X., Wang, L.: Handwritten Chinese character recognition system based on neural network convolution depth. Comput. Eng. Appl. 53(10), 246–250 (2017)

    Google Scholar 

  7. Lee, S.-G., Sung, Y., Kim, Y.-G., Cha, E.-Y.: Variations of AlexNet and GoogLeNet to improve Korean character recognition performance. J. Inf. Process. Syst. 14(1), 205–217 (2018)

    Google Scholar 

  8. Nguyen, C.K., Nguyen, C.T., Masaki, N.: Tens of thousands of Nom character recognition by deep convolution neural networks. In: Historical Document Imaging and Processing, pp. 37–41 (2017)

    Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  10. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  11. Cho, K., Courville, A., Bengio, Y.: Describing multimedia content using attention-based encoder-decoder networks. Multimedia 17(11), 1875–1886 (2015)

    Google Scholar 

  12. Kabade, A.L., Sangam, V.G.: Canny edge detection algorithm. IJARECE 5(5), 12–16 (2016)

    Google Scholar 

  13. Garla, V.N., Brandt, C.: Ontology-guided feature engineering for clinical text classification. J. Biomed. Inform. 45(6), 992–998 (2012)

    Article  Google Scholar 

  14. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 6245(349), 255–260 (2015)

    Article  MathSciNet  Google Scholar 

  15. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: ICML-14, pp. 1764–1772 (2014)

    Google Scholar 

  16. Sanchez, J., Romero, V., Toselli, A., Vidal, E.: ICFHR2014 competition on handwritten text recognition on transcriptorium datasets. In: ICFHR, pp. 181–186 (2014)

    Google Scholar 

  17. Wang, Q.-F., Yin, F., Liu, C.-L.: Handwritten Chinese text recognition by integrating multiple contexts. Pattern Anal. Mach. Intell. 32(8), 1469–1481 (2012)

    Article  Google Scholar 

  18. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Sig. Process. 7(3), 197–287 (2014)

    Article  MathSciNet  Google Scholar 

  19. He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on image-net classification. In: ICCV, pp. 1026–1034 (2015)

    Google Scholar 

  20. Kalayeh, M.M., Gong, B., Shah, M.: Improving facial attribute prediction using semantic segmentation. In: CVPR, pp. 6942–6950 (2017)

    Google Scholar 

  21. Yang, L., Yu, X., Wang, J., Liu, Y.: Research on recurrent neural network. Comput. Appl. 38(22), 16–26 (2018)

    Google Scholar 

  22. Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: CASIA online and offline Chinese handwriting databases. In: ICDAR, pp. 37–41 (2011)

    Google Scholar 

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Acknowledgment

Our work is supported by the national key research and development program (No. 2017YFC1703300) of China.

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Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100124, China

    Xinfeng Zhang & Kunpeng Yan

Authors
  1. Xinfeng Zhang
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  2. Kunpeng Yan
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Corresponding author

Correspondence to Kunpeng Yan .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zhang, X., Yan, K. (2019). An Algorithm of Bidirectional RNN for Offline Handwritten Chinese Text Recognition. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_39

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

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