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A Novel Method for Offline Handwritten Chinese Character Recognition Under the Guidance of Print

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Advances in Knowledge Discovery and Data Mining (PAKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12713))

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Abstract

In this paper, we present a new method that views offline handwritten chinese character recognition (HCCR) as a Re-identification (ReID) task. We introduce a print dataset as the target that needs to be retrieved, and make the test set of offline HCCR as the object of interest. According to ReID’s scene, the goal is to find the most similar print sample as the prediction result for each object of interest. We also employ triplet loss for metric learning, and train model together with cross-entropy loss, which has a good effect on improving performance. Compared with the classification model, the experimental results show that our method achieves much better results in few-shot learning, whose dataset is randomly selected from overall datasets. When the training set used is 5% of HWDB1.1, the gap between them even reached 9.8%. At the same time, it also obtains an accuracy of 97.69% on ICDAR-2013 offline HCCR competition dataset.

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References

  1. Casey, R., Nagy, G.: Recognition of printed Chinese characters. IEEE Trans. Electron. Comput. 1, 91–101 (1966)

    Article  Google Scholar 

  2. Chang, X., Hospedales, T.M., Xiang, T.: Multi-level factorisation net for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2109–2118 (2018)

    Google Scholar 

  3. Cheng, C., Zhang, X.Y., Shao, X.H., Zhou, X.D.: Handwritten Chinese character recognition by joint classification and similarity ranking. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 507–511. IEEE (2016)

    Google Scholar 

  4. Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)

    Google Scholar 

  5. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  6. Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J.G., Han, K.: Beyond human parts: dual part-aligned representations for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3642–3651 (2019)

    Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  8. He, L., Liao, X., Liu, W., Liu, X., Cheng, P., Mei, T.: FastReID: a Pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631 (2020)

  9. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  10. Kalayeh, M.M., Basaran, E., Gökmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1062–1071 (2018)

    Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  12. Li, Z., Xiao, Y., Wu, Q., Jin, M., Lu, H.: Deep template matching for offline handwritten Chinese character recognition. J. Eng. 2020(4), 120–124 (2020)

    Article  Google Scholar 

  13. 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 

  14. 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 

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

    Google Scholar 

  16. Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  17. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  18. Rubinstein, R.: The cross-entropy method for combinatorial and continuous optimization. Methodol. Comput. Appl. Probab. 1(2), 127–190 (1999)

    Article  MathSciNet  Google Scholar 

  19. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)

    Google Scholar 

  20. Wang, D., Zhang, S.: Unsupervised person re-identification via multi-label classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10981–10990 (2020)

    Google Scholar 

  21. Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L.: Joint learning of single-image and cross-image representations for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1288–1296 (2016)

    Google Scholar 

  22. Wang, T., Xie, Z., Li, Z., Jin, L., Chen, X.: Radical aggregation network for few-shot offline handwritten Chinese character recognition. Pattern Recogn. Lett. 125, 821–827 (2019)

    Article  Google Scholar 

  23. Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (2007)

    Google Scholar 

  24. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)

    Google Scholar 

  25. Wu, L., Shen, C., van den Hengel, A.: PersonNet: person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255 (2016)

  26. Wu, S., Chen, Y.C., Li, X., Wu, A.C., You, J.J., Zheng, W.S.: An enhanced deep feature representation for person re-identification. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–8. IEEE (2016)

    Google Scholar 

  27. Xiao, Y., Meng, D., Lu, C., Tang, C.K.: Template-instance loss for offline handwritten Chinese character recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 315–322. IEEE (2019)

    Google Scholar 

  28. Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1464–1470. IEEE (2013)

    Google Scholar 

  29. Zhang, R., Wang, Q., Lu, Y.: Combination of ResNet and center loss based metric learning for handwritten Chinese character recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 5, pp. 25–29. IEEE (2017)

    Google Scholar 

  30. Zhang, X.Y., Bengio, Y., Liu, C.L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recogn. 61, 348–360 (2017)

    Article  Google Scholar 

  31. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

    Google Scholar 

  32. Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)

  33. Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1367–1376 (2017)

    Google Scholar 

  34. Zhong, Z., Zhang, X.Y., Yin, F., Liu, C.L.: Handwritten Chinese character recognition with spatial transformer and deep residual networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3440–3445. IEEE (2016)

    Google Scholar 

  35. Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157–5166 (2018)

    Google Scholar 

  36. Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Data Science and Engineering, East China Normal University, Shanghai, China

    Keping Yan & Jun Guo

  2. DongQi AI Co., Ltd., Nanjing, China

    Weiqing Zhou

Authors
  1. Keping Yan
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  2. Jun Guo
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  3. Weiqing Zhou
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Corresponding author

Correspondence to Jun Guo .

Editor information

Editors and Affiliations

  1. IIIT, Hyderabad, Hyderabad, India

    Kamal Karlapalem

  2. Chinese University of Hong Kong, Shatin, Hong Kong

    Hong Cheng

  3. Virginia Tech, Arlington, VA, USA

    Naren Ramakrishnan

  4. Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  6. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  7. IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

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Yan, K., Guo, J., Zhou, W. (2021). A Novel Method for Offline Handwritten Chinese Character Recognition Under the Guidance of Print. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-75765-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75764-9

  • Online ISBN: 978-3-030-75765-6

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