A Graph based Calligraphy Similarity Compare Model

被引：1

作者：

Pan, Guoyang ^{[1
]}

Yang, Yi ^{[2
]}

Li, Meng ^{[2
]}

Hu, Xueyang ^{[3
]}

Huang, Weixing ^{[2
,4
]}

Wang, Jian ^{[2
]}

Wang, Yun ^{[2
]}

机构：

[1] Chinese Acad Sci, Sch Artificial Intelligence, Univ Chinese Acad Sci, Inst Automat, Beijing, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China

[3] Univ Maryland, College Pk, MD 20742 USA

[4] CASIA Junsheng Shenzhen Intelligent & Big Data Sc, Shenzhen, Peoples R China

来源：

2021 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2021) | 2021年

关键词：

calligraphy estimation; graph similarity; image process; graph neural network;

D O I：

10.1109/QRS-C55045.2021.00065

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Calligraphy is one of the most famous traditional art in China. The Calligraphy copying practice is the inevitable phase when learning Calligraphy. Calligraphy character has structure and stroke attributes, such as length of stroke and the position distribution of subpart, which can identify each certain character. In this paper, we propose a graph neural network-based algorithm which can measure the similarity between two Calligraphy characters according to structure and stoke. Experiment shows that the proposed method gives satisfied results with respect to the similarity measurement for the Calligraphy copying practice.

引用

页码：395 / 400

页数：6

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