Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City

Abstract

This work intends to solve the problem that the current artistic typeface generation methods rely too much on manual intervention, lack novelty, and the single font local feature and the global feature extraction method cannot fully describe the font features. Firstly, it proposes a handwritten word recognition model based on generalized search trees (GIST) and the pyramid histogram of oriented gradient (PHOG). The local features and global features of the font are fused. Secondly, a model of automatic artistic typeface generation based on generative adversarial networks (GAN) is constructed, which can use hand-drawn fonts to automatically generate artistic typefaces in the desired style through training as needed. Finally, the generation of the huaniao typeface is used as an example. By constructing the dataset, the effectiveness of the two models is verified. The experimental results show the following: (1) The proposed handwritten character recognition model based on GIST and PHOG has a higher recognition rate of different fonts than the single GIST and PHOG features by more than 5.8%. The total recognition time is reduced by more than 49.4%, and the performance is improved significantly. (2) Compared with other popular algorithms, the constructed GAN-based automatic artistic typeface generation model has the best quality of the generation of huaniao on both the pencil sketch and the calligraphy character image dataset. Models have broad application prospects in contemporary advertising text art design. This study aims to provide important technical support for the automation of contemporary advertising text art design and the improvement of overall efficiency.