Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence

• URL: http://arxiv.org/abs/2403.18183v1
• Date: Wed, 27 Mar 2024 01:21:48 GMT
• Title: Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence
• Authors: Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, Shubham Nitin Mulay,
• Abstract summary: A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. While state-of-the-art document AI models demonstrate the benefits of incorporating layout and image data, it remains unclear whether the nuances of document aesthetics are effectively captured.
• Score: 3.049887057143419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. Thoughtful document design, informed by psychological insights, enhances both the visual appeal and the comprehension of the content. While state-of-the-art document AI models demonstrate the benefits of incorporating layout and image data, it remains unclear whether the nuances of document aesthetics are effectively captured. To bridge the gap between human cognition and AI interpretation of aesthetic elements, we formulated hypotheses concerning AI behavior in document understanding tasks, specifically anchored in document design principles. With a focus on legibility and layout quality, we tested four aspects of aesthetic effects: noise, font-size contrast, alignment, and complexity, on model confidence using correlational analysis. The results and observations highlight the value of model analysis rooted in document design theories. Our work serves as a trailhead for further studies and we advocate for continued research in this topic to deepen our understanding of how AI interprets document aesthetics.

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