# Evaluation Metrics for Automated Typographic Poster Generation

arXiv: https://arxiv.org/abs/2402.06945 · HTML: https://mlsanity.com/paper/2402.06945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Learning Visual Importance for Graphic Designs and Data Visualizations](https://mlsanity.com/paper/1708.02660/) (2017)
- [Agile (data) science: a (draft) manifesto](https://mlsanity.com/paper/2104.12545/) (2021)

## Abstract (arXiv)

Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task. In this paper, we propose a set of heuristic metrics for typographic design evaluation, focusing on their legibility, which assesses the text visibility, aesthetics, which evaluates the visual quality of the design, and semantic features, which estimate how effectively the design conveys the content semantics. We experiment with a constrained evolutionary approach for generating typographic posters, incorporating the proposed evaluation metrics with varied setups, and treating the legibility metrics as constraints. We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach and the visual characteristics outputs.