Font Generation and Keypoint Ranking for Stroke Order of Chinese Characters by Deep Neural Networks

被引：0

作者：

Li H.-T. ^{[1
]}

Jiang M.-X. ^{[1
]}

Huang T.-T. ^{[1
]}

Chiang C.-K. ^{[1
]}

机构：

[1] Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, No.168, Sec. 1, University Rd., Minhsiu

来源：

SN Computer Science | 2021年 / 2卷 / 4期

基金：

以色列科学基金会;

关键词：

Autoencoder; Learning to rank; Stroke extraction; Stroke order of Chinese character; Style transfer;

D O I：

10.1007/s42979-021-00717-2

中图分类号：

学科分类号：

摘要：

Determining the stroke order of a Chinese character image is challenging, because there is no explicit representation for image to sequence learning. This paper investigates the approach in Chinese character generation given just a few image samples of a specific font. Then, keypoint extraction for stroke decomposition and learning to rank method are proposed for obtaining stroke order. Since the same character can appear in multiple fonts, different font of Chinese character has distinct keypoints. Thus, it brings difficulties in acquiring stroke order. Generative Adversarial Networks (GANs) is introduced to generate lots of Chinese character images with different fonts for training and testing the proposed method. The keypoint ranking model based on stroke extraction combining font transfer based on GANs is proposed to complete this task. Compared to other methods, our method can be accomplished without human annotation as initial hints in prediction stage. The experimental results demonstrate the effectiveness of our method that achieved 0.9667 NDCG in average and up to 29.53% samples are higher than 0.98 NDCG. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

共 20 条

[1]

Cao Z., Qin T., Liu T.Y., Tsai M.F., Li H., Learning to rank: From pairwise approach to listwise approach, Proceedings of the 24Th International Conference on Machine Learning, pp. 129-136, (2007)

[2]

Chang H., Lu J., Yu F., Finkelstein A., Pairedcyclegan: Asymmetric style transfer for applying and removing makeup, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 40-48, (2018)

[3]

Chang J., Gu. Y., Chinese typography transfer, [Preprint]., (2017)

[4]

Hayashi H., Abeglyphgan K.S., Style-Consistent Font Generation Based on Generative Adversarial Networks. Knowledge Based System, 186, (2019)

[5]

Ioffe S., Szegedy C., Batch normalization: Acelerating deep network training by reducing internal covariate shift, In Proceedings of the 32Nd International Conference on Maching Learning., 37, pp. 448-456, (2015)

[6]

Isola P., Zhu J.Y., Zhou T., Efros A.A., Image-to-image translation with conditional adversarial networks, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., pp. 1125-1134, (2017)

[7]

Jarvelin K., Kekalainen J., Cumulated gain-based evaluation of IR techniques, ACM Trans Inf Syst (TOIS)., 20, 4, pp. 422-446, (2002)

[8]

Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Commun ACM., 60, 6, pp. 84-90, (2017)

[9]

Lin F., Tang X., Off-line handwritten chinese character stroke extraction, Object Recognit Support User Interact Serv Robots, 3, pp. 249-252, (2002)

[10]

Mueller S., Huebel N., Waibel M., D'Andrea R., Robotic calligraphy-learning how to write single strokes of Chinese and Japanese characters, In IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 1734-1739, (2013)

← 1 2 →