Khattat: Enhancing Readability and Concept Representation of Semantic Typography
Ahmed Hussein, Alaa Elsetohy, Sama Hadhoud, Tameem Bakr, Yasser, Rohaim, and Badr AlKhamissi

TL;DR
Khattat is an automated system that combines language models, semantic font understanding, and diffusion-based stylization to create expressive, readable typography that visually conveys word meanings across languages.
Contribution
The paper introduces an end-to-end system integrating LLMs, FontCLIP, and diffusion models for semantic typography, enhancing readability and expressiveness automatically.
Findings
Improves readability of stylized typography across languages
Automatically generates concept-representative typography designs
Outperforms baselines in readability and versatility
Abstract
Designing expressive typography that visually conveys a word's meaning while maintaining readability is a complex task, known as semantic typography. It involves selecting an idea, choosing an appropriate font, and balancing creativity with legibility. We introduce an end-to-end system that automates this process. First, a Large Language Model (LLM) generates imagery ideas for the word, useful for abstract concepts like freedom. Then, the FontCLIP pre-trained model automatically selects a suitable font based on its semantic understanding of font attributes. The system identifies optimal regions of the word for morphing and iteratively transforms them using a pre-trained diffusion model. A key feature is our OCR-based loss function, which enhances readability and enables simultaneous stylization of multiple characters. We compare our method with other baselines, demonstrating great…
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Taxonomy
TopicsSubtitles and Audiovisual Media · Translation Studies and Practices · Natural Language Processing Techniques
MethodsDiffusion
