Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network

Author:

Yan Fei12,Lan Xueping1,Zhang Hua1,Li Linjing1ORCID

Affiliation:

1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

2. Mianyang Polytechnic, Mianyang 621000, China

Abstract

The essence of Chinese calligraphy inheritance resides in calligraphy education. However, it encounters challenges such as a scarcity of calligraphy instructors, time-consuming and inefficient manual assessment methods, and inconsistent evaluation criteria. In response to these challenges, this paper introduces a deep learning-based automatic calligraphy evaluation model. Initially, hard-pen handwriting samples from 100 volunteers were collected and preprocessed to create a dataset consisting of 4800 samples, along with the corresponding label files for hard-pen calligraphy evaluation. Subsequently, YOLOv5 was utilized for region detection and character recognition on the evaluation samples to obtain the corresponding standard samples. Lastly, a Siamese metric model, with VGG16 as the primary feature extraction submodule, was developed for hard-pen calligraphy evaluation. To improve feature extraction and propagation, a transformer structure was introduced to extract global information from both the evaluated and standard samples, thereby optimizing the evaluation results. Experimental results demonstrate that the proposed model achieves a precision of 0.75, recall of 0.833, and mAP of 0.990 on the hard-pen calligraphy evaluation dataset, effectively realizing automatic calligraphy evaluation. This model presents a novel approach for intelligently assessing hard-pen calligraphy.

Funder

Mianyang Polytechnic Foundation for Science and Technology

Publisher

MDPI AG

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