Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation

TL;DR

This survey consolidates artistic text generation into two core streams: artistic text stylization, which adds visual effects to text, and semantic typography, which reshapes text to visually reflect meaning, including dynamic motion. It covers static and dynamic modalities, detailing patch-based, GAN-based, and diffusion-based approaches, and analyzes datasets and tailored evaluation metrics for artistic text tasks. The paper presents a comprehensive taxonomy of methods (text effect transfer, arbitrary style transfer, font-text joint transfer, and kinetic/semantic typography) and discusses practical applications in graphic design and scene text editing. It also identifies key challenges—abstract-concept guidance, slow diffusion sampling, limited dynamic-data resources, and the need for fine-grained, interactive control—to guide future research and development in this field.

Abstract

Artistic text generation aims to amplify the aesthetic qualities of text while maintaining readability. It can make the text more attractive and better convey its expression, thus enjoying a wide range of application scenarios such as social media display, consumer electronics, fashion, and graphic design. Artistic text generation includes artistic text stylization and semantic typography. Artistic text stylization concentrates on the text effect overlaid upon the text, such as shadows, outlines, colors, glows, and textures. By comparison, semantic typography focuses on the deformation of the characters to strengthen their visual representation by mimicking the semantic understanding within the text. This overview paper provides an introduction to both artistic text stylization and semantic typography, including the taxonomy, the key ideas of representative methods, and the applications in static and dynamic artistic text generation. Furthermore, the dataset and evaluation metrics are introduced, and the future directions of artistic text generation are discussed. A comprehensive list of artistic text generation models studied in this review is available at https://github.com/williamyang1991/Awesome-Artistic-Typography/.

Figures (9)

• Figure 1: Artistic text generated by TET-GAN yang2019tet, ShapeMatching GAN yang2019shapematching, DS-Fusion tanveer2023ds and Zou et al. zou2016legible. Artistic text generation can be broadly classified into two categories: artistic text stylization and semantic typography.
• Figure 2: Artistic text generated by Stable Diffusion 3 with the prompt: Epic anime artwork of a wizard atop a mountain at night casting a cosmic spell into the dark sky that says "Stable Diffusion 3" made out of colorful energy. Image credits: Stable Diffusion 3 (https://stability.ai/news/stable-diffusion-3)
• Figure 3: Artistic text stylization can be divided into (a) text effect transfer and (b) arbitrary style transfer on text based on whether the style input $S'$ is a well-designed text effect image or an arbitrary style image. Image credits: T-Effect yang2017awesome and UT-Effect yang2018context.
• Figure 4: Taxonomy of the representative artistic text generation methods and applications. The blue background and red background indicate methods for static and dynamic artistic text generation, respectively.
• Figure 5: Different frameworks for artistic text stylization. Image credits: Goda et al. Goda2010TextureTB, T-Effect yang2017awesome, TET-GAN yang2019tet.