Semantic typography balances word clarity and image recognition
MSTypography: Multi-character Semantic Typography via Balancing Word Legibility and Object Recognizability
Computer Vision and Pattern RecognitionGraphics
Summary
Making words look like pictures can be tricky because the letters still need to be readable. The authors built a method that changes entire words instead of just single letters, keeping the words clear while making them resemble the desired shapes. They use a two-step process: first matching the overall shape, then refining individual letters, while checking that the text remains readable by OCR. Their approach works across several languages and improves over previous methods.
What this means in practice
- •For graphic designers: Create visually meaningful text-based designs that maintain readability for multilingual projects.
- •For advertising teams: Develop eye-catching logos and branding that integrate word shapes with symbolic imagery effectively.
Authors
Xinye Yang, Xinding Zhu, Kai Fang, Xinyi Ren, Mengjian Li, Bin Cao, Jiazhou Chen
Abstract
Semantic typography is a design technique where the visual representation of a word conveys its semantic meaning, while maintaining its legibility. Existing digital typography methods mainly focus on single-character scenarios. They suffer from a lack of legibility constraints and insufficient local deformation when extended to multi-character words, as the intricate structures among multiple characters are hardly preserved during the typography process. In this paper, we propose a global-to-local typography framework for multi-character scenarios. It performs mask-driven silhouette approximation at the global level, while semantic-guided refinement at the local level, with a culling step in between to improve efficiency. To preserve word legibility, we designed structural losses (including explicit collision constraints and implicit Jacobian singular value constraints) and an OCR constraint for character-level readability. To enhance the object recognizability, we leverage semantic guidance with diffusion priors, which drives the character glyph toward the target concept while preserving its structural integrity. To the best of our knowledge, this is the first multi-character semantic typography method that effectively balances word legibility and object recognizability. Evaluations on five representative languages (English, Chinese, Japanese, Korean, Arabic) demonstrate superiority over SOTA methods. Codes will be open-sourced.