Authenticity Identification Method for Calligraphy Regular Script Based on Improved YOLOv7 Algorithm Provisionally Accepted
Zucheng Huang1
Xueyao Jiang1
Hai Yuan1
Weijun Wang1, 2
Jian Wang1
Xintong Wang1
Zheng Xu1*
- 1Guangzhou Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), China
- 2SHENZHEN CAS DERUI INTELLIGENT TECHNOLOGY CO.,LTD, China
A regular calligraphy script of each calligrapher has unique strokes, and a script's authenticity can be identified by comparing them. Hence, this study introduces a method for identifying the authenticity of regular script calligraphy works based on the improved YOLOv7 algorithm. The proposed method evaluates the authenticity of calligraphy works by detecting and comparing the number of singlecharacter features in each regular script calligraphy work. Specifically, first, we collected regular script calligraphy works from a well-known domestic calligrapher and divided each work into a single-character dataset. Then, we introduced the PConv module in FasterNet, the DyHead dynamic detection header network, and the MPDiou bounding box loss function to optimize the accuracy of the YOLOv7 algorithm. Thus, we constructed an improved algorithm named YOLOv7-PDM, which is used for regular script calligraphy identification. The proposed YOLOv7-PDM model was trained and tested using a prepared regular script single-character dataset. Through experimental results, we confirmed the practicality and feasibility of the proposed method and demonstrated that the YOLOv7-PDM algorithm model achieves 94.19% accuracy (mAP50) in detecting regular script font features, with a single-image detection time of 3.1 ms and 31.67M parameters. The improved YOLOv7 algorithm model offers greater advantages in detection speed, accuracy, and model complexity compared to current mainstream detection algorithms. This demonstrates that the developed approach effectively extracts stroke features of regular script calligraphy and provides guidance for future studies on authenticity identification.
Keywords: Calligraphy works identification, YOLOv7 algorithm, PConv module, DyHead dynamic detection head network, MPDiou loss function
Received: 21 Mar 2024;
Accepted: 29 Apr 2024.
Copyright: © 2024 Chen, Huang, Jiang, Yuan, Wang, Wang, Wang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Zheng Xu, Guangzhou Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Guangzhou, 511458, China