Evaluation Metrics for Automated Typographic Poster Generation
This addresses the challenge of evaluating computational typographic designs for designers and researchers, though it appears incremental as it builds on existing evolutionary methods with new metrics.
The paper tackles the problem of evaluating automatically generated typographic posters by proposing heuristic metrics for legibility, aesthetics, and semantic features, and experiments with a constrained evolutionary approach that incorporates these metrics to generate posters while analyzing their performance and visual characteristics.
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.