Application of Emotional Factors of Ink Symbols Evaluated by Network Model in Modern Visual Image Design

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.