Abstract
Structural features of Chinese characters provide abundant style information for handwritten style recognition, while prior work on this task has few senses of using structural information. Meanwhile, based on current handwritten Chinese character datasets, it is hard to obtain a good generalization model only by character category and writer information. Therefore, we add the structural information known as morpheme which is the smallest and unique structure in Chinese character into the large handwritten dataset HCL2000 and update it to HCL2020. We also present a deep fusion network (Morpheme-based Handwritten Style Recognition Network, M-HSRNet), capturing both overall layout characteristics and detail structural features of characters to recognize handwritten style. The evaluation results of the proposed model on HCL2020 are observed to prove the effectiveness of morpheme. Together with the proposed Morpheme Encoder module, our approach achieves an accuracy of 78.06% in handwritten style recognition, which is 3 points higher than the result without morpheme information.
P. Hu and M. Xu—contribute equally to this work.
The first authors are students.
This work was supported in part by MoE-CMCC “Artifical Intelligence” Project No.MCM20190701.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arvanitopoulos, N., Chevassus, G., Maggetti, D., Süsstrunk, S.: A handwritten French dataset for word spotting: CFRAMUZ. In: Proceedings of the 4th International Workshop on Historical Document Imaging and Processing, pp. 25–30 (2017)
Bensefia, A., Paquet, T.: Writer verification based on a single handwriting word samples. EURASIP J. Image Video Process. 2016(1), 34 (2016)
Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)
Chinese, D.M.: Chinese character structure: a guide to writing characters well. https://www.decodemandarinchinese.com/character-structure/
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., Ha, D.: Deep learning for classical Japanese literature. arXiv preprint arXiv:1812.01718 (2018)
Dai, R., Liu, C., Xiao, B.: Chinese character recognition: history, status and prospects. Front. Comput. Sci. China 1(2), 126–136 (2007)
De Campos, T.E., Babu, B.R., Varma, M., et al.: Character recognition in natural images. VISAPP (2), 7 (2009)
Deng, X., Dragotti, P.L.: Deep convolutional neural network for multi-modal image restoration and fusion. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Deng, Z., et al.: Deep multi-model fusion for single-image Dehazing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2453–2462 (2019)
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)
Jain, R., Doermann, D.: Offline writer identification using k-adjacent segments. In: 2011 International Conference on Document Analysis and Recognition, pp. 769–773. IEEE (2011)
Kamal, P., Rahman, F., Mustafiz, S.: A robust authentication system handwritten documents using local features for writer identification. J. Comput. Sci. Eng. 8(1), 11–16 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lee, G., Nho, K., Kang, B., Sohn, K.A., Kim, D.: Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci. Rep. 9(1), 1–12 (2019)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recogn. 46(1), 155–162 (2013)
Olivier, R., Hanqiang, C.: Nearest neighbor value interpolation. Int. J. Adv. Comput. Sci. Appl. 3(4), 25–30 (2012)
Ren, J., Guo, J.: The new edition of HCL2000 and its application (in Chinese). J. Chin. Inf. Process. 19(5), 99–106 (2005)
Sermanet, P., Chintala, S., LeCun, Y.: Convolutional neural networks applied to house numbers digit classification. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 3288–3291. IEEE (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Wang, D.H., Liu, C.L., Yu, J.L., Zhou, X.D.: CASIA-OLHWDB1: A database of online handwritten Chinese characters. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 1206–1210. IEEE (2009)
Yadav, C., Bottou, L.: Cold case: The lost MNIST digits. In: Advances in Neural Information Processing Systems, pp. 13443–13452 (2019)
Yang, W., Jin, L., Liu, M.: DeepWriterID: an end-to-end online text-independent writer identification system. IEEE Intell. Syst. 31(2), 45–53 (2016)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Zhang, H., Guo, J., Chen, G., Li, C.: HCL 2000-a large-scale handwritten Chinese character database for handwritten character recognition. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 286–290. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, P., Xu, M., Wu, M., Chen, G., Zhang, C. (2020). Handwritten Style Recognition for Chinese Characters on HCL2020 Dataset. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-60639-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60638-1
Online ISBN: 978-3-030-60639-8
eBook Packages: Computer ScienceComputer Science (R0)