# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 1 Introduction  
Learning calligraphy encapsulates the finest understanding and continuation of traditional Chinese culture. However, the resources for calligraphy education remain relatively limited, and the pressing issue of an insufficient number of specialized calligraphy instructors is conspicuous. Calligraphy evaluation holds the potential to spark enthusiasm for calligraphy learning, yet it faces challenges such as a scarcity of renowned mentors, low evaluation efficiency, and disparities in evaluation criteria. Influenced by the rapid advancement of computer and information technology, traditional calligraphy is becoming digitized in terms of creative tools, artistic effects, writing techniques, and creative methodologies, culminating in the emergence of the interdisciplinary domain of digital calligraphy research. Digital calligraphy research primarily comprises three facets: (1) digitized modeling of calligraphy tools; (2) analysis and processing of calligraphy images; and (3) representation and synthesis of calligraphic characters. These three directions within digital calligraphy research focus on a shared concern: the computational aesthetics and evaluation of calligraphy. Intelligent aesthetic evaluation poses a challenging problem within the field of artificial intelligence. Current efforts in aesthetic evaluation primarily focus on aesthetic ratings of photographic images [1], aesthetic evaluation of painted artwork images [2], aesthetic evaluation of videos [3], and aesthetic evaluation of web page layout design [4]. However, there is a relative scarcity of research on computational aesthetics and evaluation of calligraphy. The existing related research predominantly branches into twodirections, calligraphy evaluation based on feature engineering and calligraphy evaluation based on deep learning. The application of feature engineering in calligraphy evaluation commonly integrates manually extracted features with classifiers. Common artificial features, based on traditional visual theories, include SIFT [5], HOG [6], and Gabor [7], among others. These features, extracted from images, subsequently undergo common machine learning methods for classification, such as SVM [8], RF [9], KNN [10], Xgboost [11], and LightGBM [12]. [PERSON] et al. [13] introduced an interactive calligraphy guidance system, leveraging image-processing techniques to extract quantifiable features such as the center, size, and projection of each handwritten character. These features are then subjected to fuzzy inference to render evaluations of the handwritten characters. [PERSON] et al. [14] designed eight directional features to implement Chinese handwritten quality evaluations by analyzing confidence in online Chinese handwritten recognition. [PERSON] et al. [15] represented the topology of Chinese characters using WF histograms and employed these topological features as inputs for calligraphy evaluation via an Adaboost ensemble composed of support vector regression (SVR). Drawing inspiration from classical calligraphy principles, [PERSON] et al. [16] proposed 22 global shape features. Incorporating a 10-dimensional feature vector for sparse coding to capture component layout information, these features are concatenated and input into an artificial neural network for calligraphy evaluation. [PERSON] et al. [17] introduced a hierarchical evaluation approach that encompasses evaluations of entire characters and individual strokes. This approach computes comprehensive weighted similarities and transforms them into final evaluation scores. In subsequent research efforts, the team employed a skeleton-based iterative nearest point algorithm to synthesize overall and stroke-level similarities, yielding ultimate comprehensive evaluation scores [18]. [PERSON] et al. [19] devised three feature criteria based on Chinese calligraphy theory for extracting features of Chinese characters from calligraphy teaching materials. Subsequently, they employed a possibility-probability distribution method to implement calligraphy evaluation.