mlsanityData to 7 Oct 2026

What Text Design Characterizes Book Genres?

arXiv 2402.16356 ↗First posted 26 Feb 2024cs.CVcited by 2

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Abstract (arXiv)

This study analyzes the relationship between non-verbal information (e.g., genres) and text design (e.g., font style, character color, etc.) through the classification of book genres using text design on book covers. Text images have both semantic information about the word itself and other information (non-semantic information or visual design), such as font style, character color, etc. When we read a word printed on some materials, we receive impressions or other information from both the word itself and the visual design. Basically, we can understand verbal information only from semantic information, i.e., the words themselves; however, we can consider that text design is helpful for understanding other additional information (i.e., non-verbal information), such as impressions, genre, etc. To investigate the effect of text design, we analyze text design using words printed on book covers and their genres in two scenarios. First, we attempted to understand the importance of visual design for determining the genre (i.e., non-verbal information) of books by analyzing the differences in the relationship between semantic information/visual design and genres. In the experiment, we found that semantic information is sufficient to determine the genre; however, text design is helpful in adding more discriminative features for book genres. Second, we investigated the effect of each text design on book genres. As a result, we found that each text design characterizes some book genres. For example, font style is useful to add more discriminative features for genres of ``Mystery, Thriller & Suspense'' and ``Christian books & Bibles.''

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 26 Feb 2024.

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13 of 26 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2020Quantifying Attention Flow in Transformerscited by 759
  4. 2019Character Region Awareness for Text Detectioncited by 273
  5. 2019What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysiscited by 191
  6. 2021SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Modelscited by 43
  7. 2022Aesthetic Text Logo Synthesis via Content-aware Layout Inferringcited by 19
  8. 2020Let Me Choose: From Verbal Context to Font Selectioncited by 8
  9. 2019Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisementscited by 7
  10. 2020Effect of Text Color on Word Embeddingscited by 4
  11. 2020Deep multi-modal networks for book genre classification based on its covercited by 3
  12. 2021Font Style that Fits an Image -- Font Generation Based on Image Contextcited by 3
1 more
  1. 2023Analyzing Font Style Usage and Contextual Factors in Real Imagescited by 3

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