Deep Neural Network Model-Assisted Reconstruction and Optimization of Chinese Characters in Product Packaging Graphic Patterns and Visual Styling Design

Abstract

Chinese character fonts not only carry the long history of Chinese civilization, but also burst out modern design art elements with distinctive Chinese characteristics. This article first analyzes the origin and writing form of several ancient Chinese characters and draws out the influence of the historical evolution of ancient Chinese characters on Chinese culture. In the basic theoretical structure of font design, traditional art elements and modern font design are integrated, and specific design cases are analyzed. A Chinese character packaging quality detection method combining machine vision and a lightweight convolutional neural network is proposed. First, the method based on threshold segmentation and affine transformation in machine vision is used to perform threshold processing on the image to be tested, and the Chinese character region is tilted and cropped; then, the network structure of the classification algorithm is designed according to the requirements of image features and defect recognition. The field images are produced, a dataset of Chinese character packaging defects is established, and then the proposed Chinese character packaging defect recognition network is verified and deployed to test the accuracy and detection speed of the algorithm deployed on the Jetson Nano embedded platform. Combined with theoretical research and case analysis, the design of packaging design series is practiced with the idea of combining Chinese character art design and classical culture.