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Khattat: Enhancing Readability and Concept Representation of Semantic Typography

Ahmed Hussein, Alaa Elsetohy, Sama Hadhoud, Tameem Bakr, Yasser Rohaim, Badr AlKhamissi

TL;DR

An end-to-end system that automates expressive typography that enhances readability and enables simultaneous stylization of multiple characters, and is compared with other baselines, demonstrating great readability enhancement and versatility across multiple languages and writing scripts.

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 readability enhancement and versatility across multiple languages and writing scripts.

Khattat: Enhancing Readability and Concept Representation of Semantic Typography

TL;DR

An end-to-end system that automates expressive typography that enhances readability and enables simultaneous stylization of multiple characters, and is compared with other baselines, demonstrating great readability enhancement and versatility across multiple languages and writing scripts.

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 readability enhancement and versatility across multiple languages and writing scripts.
Paper Structure (30 sections, 6 equations, 19 figures, 1 table, 1 algorithm)

This paper contains 30 sections, 6 equations, 19 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Semantic Typography
  4. LLMs and Their Applications
  5. Diffusion Models and Their Applications
  6. Preliminaries
  7. Vector Representation of Letters
  8. Diffusion Models and Score Distillation Sampling
  9. Methodology
  10. Problem formulation
  11. Prompt Engine
  12. Font Selection
  13. Region Selection
  14. Morphing pipeline
  15. Initialization
  16. ...and 15 more sections

Figures (19)

  • Figure 1: Examples of semantic typography generated by Khattat in Arabic and English. Coloured examples are post-processed using Stable diffusion's depth-to-image method.
  • Figure 2: Overview of the Khattat system. It uses a prompt engine to generate concept and font prompts, selects a suitable font with FontCLIP, and identifies regions for morphing. Over 500 iterations, it deforms letter outlines to align with the concept while ensuring readability using the OCR loss constraint, and minimizing distortions.
  • Figure 3: The Region selection procedure of Khattat for the word "Bird" in Arabic. For each substring, we run a lighter optimization pipeline of 100 iterations and score each substring, Highest scoring region is used for the full optimization pipeline.
  • Figure 4: The stylized Arabic word "castle" with SVG control points shown in green and connected by red lines. The left-most character exhibits distortion caused by frequent intersections of Bezier curves, visible in the figure.
  • Figure 5: Constrained Delaunay triangulation of the Arabic word "Growth".
  • ...and 14 more figures