{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:34:54Z","timestamp":1782966894680,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CQUniversity Research Internal Grants"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu) scripts. Beginners often start with the regular script, advancing to more intricate styles like cursive. Each style, marked by unique historical calligraphy contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification is pivotal. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphy styles from the Tang dynasty (690\u2013907 A.D.), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5\u201396.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in the categorization of both font and artistic styles. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.<\/jats:p>","DOI":"10.3390\/s24010197","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T03:28:41Z","timestamp":1703820521000},"page":"197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font"],"prefix":"10.3390","volume":"24","author":[{"given":"Qing","family":"Huang","sequence":"first","affiliation":[{"name":"School of Education and the Arts, Central Queensland University, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6019-1035","authenticated-orcid":false,"given":"Michael","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9609-9577","authenticated-orcid":false,"given":"Dan","family":"Agustin","sequence":"additional","affiliation":[{"name":"Centre of Railway Engineering, School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2220-2389","authenticated-orcid":false,"given":"Lily","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9854-8426","authenticated-orcid":false,"given":"Meena","family":"Jha","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, Q., and Balsys, R.J. (2009, January 11\u201314). Applying Fractal and Chaos Theory to Animation in the Chinese Literati Tradition. Proceedings of the Sixth International Conference on Computer Graphics, Imaging and Visualization, Tianjin, China.","DOI":"10.1109\/CGIV.2009.56"},{"key":"ref_2","unstructured":"Fitzgerald, C.P. (1969). The Horizon History of China, American Heritage Publishing Co., Inc."},{"key":"ref_3","unstructured":"Wong, E. (1997). The Shambhala Guide to Taoism, Shambhala Publications, Inc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_5","unstructured":"Liu, S. (2003). Full Colour Art History of Chinese Calligraphy, Ningxia People\u2019s Publishing House. [1st ed.]."},{"key":"ref_6","unstructured":"Li, W. (2013). Research on Key Technologies of Chinese Calligraphy Synthesis and Recognition for Chinese Character of Video. [Ph.D. Thesis, School of Information Science and Engineering, Xiamen University]."},{"key":"ref_7","unstructured":"Lin, Y. (2014). Research and Application of Chinese Calligraphic Character Recognition. [Ph.D. Thesis, College of Computer Science, Zhejiang University]."},{"key":"ref_8","unstructured":"Mao, T.J. (2014). Calligraphy Writing Style Recognition. [Ph.D. Thesis, College of Computer Science, Zhejiang University,]."},{"key":"ref_9","first-page":"39","article-title":"Calligraphy style identification based on visual features","volume":"21","author":"Wang","year":"2016","journal-title":"Mod. Comput."},{"key":"ref_10","unstructured":"Yan, Y.F. (2018). Calligraphy Style Recognition Based on CNN. [Ph.D. Thesis, College of Information and Computer, Taiyuan University of Technology]."},{"key":"ref_11","first-page":"1187","article-title":"Chinese calligraphy recognition system based on convolutional neural network","volume":"15","author":"Cui","year":"2021","journal-title":"ICIC Express Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, L. (2021, January 27\u201328). Research and Application of Chinese Calligraphy Character Recognition Algorithm Based on Image Analysis. Proceedings of the 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China.","DOI":"10.1109\/AEECA52519.2021.9574199"},{"key":"ref_13","first-page":"585","article-title":"Fake Calligraphy Recognition Based on Deep Learning","volume":"Volume 12736","author":"Liu","year":"2021","journal-title":"International Conference on Artificial Intelligence and Security, ICAIS 2021: Artificial Intelligence and Security"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhai, C., Chen, Z., Li, J., and Xu, B. (2016, January 5\u20137). Chinese image text recognition with BLSTM-CTC: A segmentation-free method. Proceedings of the 7th Chinese Conference on Pattern Recognition\u2014(CCPR), Chengdu, China.","DOI":"10.1007\/978-981-10-3005-5_43"},{"key":"ref_15","unstructured":"Li, B. (2021, May 18). Convolution Neural Network for Traditional Chinese Calligraphy Recognition. CS231N Final Project. Available online: http:\/\/cs231n.stanford.edu\/reports\/2016\/pdfs\/257Report.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wen, Y., and Sig\u00fcenza, J. (2019, January 19\u201321). Chinese calligraphy: Character style recognition based on full-page document. Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, Prague, Czech Republic.","DOI":"10.1145\/3373509.3373512"},{"key":"ref_17","first-page":"4845092","article-title":"Evaluation of Chinese calligraphy by using DBSC vectorization and ICP algorithm","volume":"2016","author":"Wang","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10032-016-0277-z","article-title":"Chinese calligraphic style representation for recognition","volume":"20","author":"Gao","year":"2017","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"key":"ref_19","unstructured":"Zou, J., Zhang, J., and Wang, L. (2019). Handwritten Chinese character recognition by convolutional neural network and similarity ranking. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yu, W., Wang, Z., Li, J., and Pan, Z. (2021, January 20\u201322). Attention-Enhanced CNN for Chinese Calligraphy Styles Classification. Proceedings of the 2021 IEEE 7th International Conference on Virtual Reality, Foshan, China.","DOI":"10.1109\/ICVR51878.2021.9483820"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, T., Chen, Y., Zhang, Z., and Li, Y.-F. (2022). EHPE: Skeleton Cues-based Gaussian Coordinate Encoding for Efficient Human Pose Estimation. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2022.3197364"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7107","DOI":"10.1109\/TII.2022.3143605","article-title":"ARHPE: Asymmetric Relation-Aware Representation Learning for Head Pose Estimation in Industrial Human\u2013Computer Interaction","volume":"18","author":"Liu","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.neucom.2020.09.068","article-title":"Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction","volume":"433","author":"Liu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1177\/13694332221127340","article-title":"Typical advances of artificial intelligence in civil engineering","volume":"25","author":"Xu","year":"2022","journal-title":"Adv. Struct. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1177\/1475921720921135","article-title":"Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer","volume":"20","author":"Xu","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","article-title":"Receptive fields and functional architecture of monkey striate cortex","volume":"195","author":"Hubel","year":"1968","journal-title":"J. Physiol."},{"key":"ref_27","unstructured":"Fukushima, K., and Miyake, S. (1982). Competition and Cooperation in Neural Nets, Springer."},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., and Courvile, A. (2016). Deep Learning, MIT Press."},{"key":"ref_29","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20135). Imagenet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing Systems Conference 2012, Lake Tahoe, NV, USA."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 12\u201315). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"146331","DOI":"10.1109\/ACCESS.2019.2946264","article-title":"MPCE: A maximum probability based cross-entropy loss function for neural network classification","volume":"7","author":"Zhou","year":"2015","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sinha, T., Haidar, A., and Verma, B. (2018, January 8\u201313). Particle swarm optimization based approach for finding optimal values of convolutional neural network parameters. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477728"},{"key":"ref_37","unstructured":"(2022, October 15). Available online: https:\/\/www.tensorflow.org\/tutorials\/images\/classification."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Szeghalmy, S., and Fazekas, A. (2023). A Comparative Study of the Use of Stratified Cross-Validation and Distribution-balanced Stratified Cross-Validation Imbalanced Learning. Sensors, 23.","DOI":"10.3390\/s23042333"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:43:49Z","timestamp":1760132629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010197"],"URL":"https:\/\/doi.org\/10.3390\/s24010197","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]}}}