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Copyright © 2022 Xiaoyan Yang et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Accurate and fast recognition of ink symbols can enhance the perception ability of intelligent ink symbols in the environment, provide information input for the operation of intelligent ink symbols, and improve the perfection of ink painting. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective. Ink symbol images generated by the simulation software are used for learning the overall and individual ink symbol detection models. Experiments demonstrate that the YOLOv3 detection algorithm has a good detection effect on ink symbolic targets, and the recognition of individual ink symbols has higher accuracy and flexibility, which provides a preliminary solution idea to solve the ink symbol information perception problem.

Details

Title
Application of Emotional Factors of Ink Symbols Evaluated by Network Model in Modern Visual Image Design
Author
Yang, Xiaoyan 1 ; Le, Fan 1   VIAFID ORCID Logo  ; Wang, Weiwei 1 ; Yang, Xiaojie 2 

 College of Art and Design, Shaanxi University of Science and Technology, Xi’an 710021, Shaanxi, China 
 Taiyuan Institute of Architectural Design and Research, Taiyuan 030000, Shanxi, China 
Editor
Zhiguo Qu
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
e-ISSN
15308677
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2630681262
Copyright
Copyright © 2022 Xiaoyan Yang et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.