# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 1 Introduction
Addressing challenges inherent in resistive touch screen handwriting, [PERSON] [20] introduced a method for fuzzy evaluation of Chinese character strokes, which entails the construction of membership templates, the selection of fuzzy subsets, and the generation of template parameters. While engineering-designed artificial features have demonstrated favorable performance across a range of evaluation tasks, the method is constrained by limitations in feature quantity and representation scope. This often leads to the omission of nuanced evaluation information in calligraphy. Furthermore, the intricate nature of manual feature design results in substantial errors and limited applicability, thus constraining the effectiveness of evaluation models. Significant progress has been achieved in image aesthetic quality evaluation through deep learning methods. For instance, in the context of aesthetic quality evaluation focused on landscape photographs in the AVA dataset, [PERSON] et al. [21] devised a multi-modal self-cooperative attention network (MSCAN). Meanwhile, [PERSON] et al. [22] utilized a deep Siamese network to introduce a multi-task deep learning framework for personalized and general image aesthetic evaluation. Due to the lack of public datasets for calligraphy evaluation, there is less research on calligraphy evaluation based on deep learning than in image aesthetic quality evaluation. In the field of calligraphy evaluation, [PERSON] et al. [23] proposed a method for categorizing the aesthetic quality of hard-pen calligraphy works by students using a CNN-based approach in 2019, achieving commendable accuracy. However, a continuous and detailed aesthetic scoring system, along with the interpretability of such scores, remains lacking. In 2022, [PERSON] et al. introduced a framework for calligraphy aesthetic evaluation through the utilization of a Siamese network architecture coupled with transfer learning, primarily focusing on brush calligraphy evaluation [24]. In these deep learning-based calligraphy evaluation methodologies, evaluation features are mainly extracted from calligraphy images, overlooking the influence of brushstroke dynamics during calligraphy creation. [PERSON] et al. [25] employed a TLD tracking algorithm to extract brush movement trajectories from video streams and proposed the MCNN-LSTM model to de rive scores of handwriting quality by comparing them with reference templates. Similarly, [PERSON] et al. [26] collected sequential data of brushstroke creation using a nine-axis sensor, and proposed a combination of long short-term memory networks with K-nearest neighbor algorithms for calligraphy evaluation through the analysis of writing motion data. Deep learning methods have the potential to enhance the efficiency, robustness, and reliability of automated calligraphy evaluation. However, it is important to note that most existing evaluation research focuses on brush calligraphy, with limited exploration into the evaluation of hard-pen calligraphy. With the ongoing development of our era, the hard-pen has gradually evolved into the primary writing instrument for modern work and life, and hard-pen calligraphy has emerged as a branch of traditional calligraphy. Distinguished from brush calligraphy by its writing tool, hard-pen calligraphy exhibits distinct aesthetic characteristics in its creations. It readily highlights the beauty of character form and structure but exhibits relative limitations in stroke formation and expressive penmanship. Capitalizing on this attribute, this paper primarily integrates Chinese character recognition and similarity calculation methods, applying deep learning models to automate hard-pen calligraphy evaluation. This endeavor seeks to achieve an automated evaluation of hard-pen calligraphy, as illustrated in the overall structure of the evaluation model depicted in Figure 1.  
The main contributions of this paper are as follows:  
1. Establishment of a dataset specifically designed for evaluating hard-pen Chinese character writing.