Aesthetic Evaluation of Chinese Calligraphy Using TabNet: Interpretability and Novel Features for Improved Accuracy

Abstract

The aesthetic evaluation of Chinese calligraphy, an art form with deep cultural roots and subjective interpretations, poses significant challenges in artificial intelligence. In this paper, we extend the methodology introduced in previous work using TabNet, a deep learning approach, to enhance the accuracy and interpretability of assessing the aesthetic qualities of Chinese calligraphy. Our study incorporates an expanded feature set: we add 10 new characteristics to the previously established 22 global shape features. This comprehensive feature ensemble captures the subtleties of Chinese calligraphy in accordance with its traditional artistic standards. Using TabNet, well known for its interpretability within deep learning frameworks, we aim to predict aesthetic scores with increased precision. We performed a rigorous evaluation using the Chinese Handwriting Aesthetic Evaluation Database. Our approach improved accuracy and elucidated the underlying reasoning behind the model's predictions, thereby enhancing transparency.