Multi-loss siamese convolutional neural network for Chinese calligraphy font and style classification-Reference-Cited by-同舟云学术

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Multi-loss siamese convolutional neural network for Chinese calligraphy font and style classification

Published:2023 Issue:8 Volume:28 Page:2370-2381
ISSN:1006-8961
Container-title:Journal of Image and Graphics
language:en
Short-container-title:

Author:

Wenyan Cheng, ,Yong Zhou,Chengying Tao,Li Liu,Zhigang Li,Taorong Qiu

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference25 articles.

1. Cao L C，Li H Q，Zhang Y J，Zhang L and Xu L. 2020. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet. Information Fusion，53:196-208［DOI:10.1016/j.inffus.2019.06.022］

2. Chen C F R，Fan Q F and Panda R. 2021. CrossViT:cross-attention multi-scale vision transformer for image classification//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision.Montreal，Canada:IEEE:347-356 ［DOI:10.1109/ICCV48922. 2021.00041］

3. Chollet F. 2017. Xception:deep learning with depthwise separable convolutions//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu，USA:IEEE:1800-1807［DOI:10.1109/CVPR.2017.195］

4. Dai F R，Tang C W and Lyu J C. 2018. Classification of calligraphy style based on convolutional neural network//Proceedings of the 25th International Conference on Neural Information Processing.Siem Reap，Cambodia:Springer:359-370［DOI:10.1007/978-3-030-04212-7_31］

5. Ding X Q，Chen L and Wu T. 2007. Character independent font recognition on a single Chinese character. IEEE Transactions on Pattern Analysis and Machine Intelligence，29（2）:195-204［DOI:10. 1109/TPAMI.2007.26］

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