Author Information
Yi Pei Lin, National Taiwan Normal University, TaiwanTzren-Ru Chou, National Taiwan Normal University, Taiwan
Abstract
This study proposes an automatic scoring approach for calligraphy images using the deep learning model ResNet. The evaluation criteria are grounded in calligraphic aesthetics, emphasizing stroke techniques and the holistic relationships of character structure. The dataset consists of elementary students’ handwriting samples and corresponding expert ratings. After preprocessing and model training, the system generates predicted scores. Experimental results show that the model achieves a low mean absolute error (MAE = 2.6) and a high quadratic weighted kappa (QWK = 0.898), indicating strong consistency between the automated scoring and expert evaluations. The proposed system provides real-time feedback and reduces teachers’ assessment workload. Future work will expand the dataset and refine the scoring dimensions to enhance the model’s applicability in calligraphy education.
Paper Information
Conference: SEACE2026Stream: Implementation & Assessment of Innovative Technologies in Education
This paper is part of the SEACE2026 Conference Proceedings (View)
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To cite this article:
Lin Y., & Chou T. (2026) A ResNet-Based Deep Learning Model for Automated Scoring of Elementary Students’ Chinese Calligraphy ISSN: 2435-5240 The Southeast Asian Conference on Education 2026: Official Conference Proceedings (pp. 371-378) https://doi.org/10.22492/issn.2435-5240.2026.29
To link to this article: https://doi.org/10.22492/issn.2435-5240.2026.29
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