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

arXiv: https://arxiv.org/abs/2407.14774 · HTML: https://mlsanity.com/paper/2407.14774/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 134 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [TextDiffuser: Diffusion Models as Text Painters](https://mlsanity.com/paper/2305.10855/) (2023)
- [AnyText: Multilingual Visual Text Generation And Editing](https://mlsanity.com/paper/2311.03054/) (2023)
- [TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering](https://mlsanity.com/paper/2311.16465/) (2023)
- [GlyphControl: Glyph Conditional Control for Visual Text Generation](https://mlsanity.com/paper/2305.18259/) (2023)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [Coherent Online Video Style Transfer](https://mlsanity.com/paper/1703.09211/) (2017)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Word-As-Image for Semantic Typography](https://mlsanity.com/paper/2303.01818/) (2023)

## Built on by (most cited first, 9 of 9)

- [FonTS: Text Rendering with Typography and Style Controls](https://mlsanity.com/paper/2412.00136/) (2024)
- [POSTA: A Go-to Framework for Customized Artistic Poster Generation](https://mlsanity.com/paper/2503.14908/) (2025)
- [WordCon: Word-level Typography Control in Scene Text Rendering](https://mlsanity.com/paper/2506.21276/) (2025)
- [ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations](https://mlsanity.com/paper/2502.10999/) (2025)
- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models](https://mlsanity.com/paper/2410.01738/) (2024)
- [MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation](https://mlsanity.com/paper/2503.02799/) (2025)
- [TextWand: A Unified Framework for Scene Text Editing](https://mlsanity.com/paper/2606.05730/) (2026)
- [Khattat: Enhancing Readability and Concept Representation of Semantic Typography](https://mlsanity.com/paper/2410.03748/) (2024)

## Abstract (arXiv)

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/.