Identification of headings in print and screen using typographic differentiation

Abstract The clear structuring of text is important for assisting readers with understanding, searching and navigating a document. Headings are a key indicator of the structure of a text, but best practice to create visual hierarchy for headings needs to be better understood. This study compared seven typographic methods for emphasizing headings in both print and screen presentations using a paired comparison study to test which methods of typographic differentiation creates headings which are easiest to identify in text. This research found that headings with the greatest typographic weight are considered easiest to identify in both print and screen-based texts.

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Publication Details

Journal
Information Design Journal
Published
2026-01-19
DOI
https://doi.org/10.1075/idj.25001.tim
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Identification of headings in print and screen using typographic differentiation

Claire Timpany
Information Design Journal
Handwritten Text Recognition Techniques
article

Identification of headings in print and screen using typographic differentiation

Claire Timpany
article en

Abstract

Abstract The clear structuring of text is important for assisting readers with understanding, searching and navigating a document. Headings are a key indicator of the structure of a text, but best practice to create visual hierarchy for headings needs to be better understood. This study compared seven typographic methods for emphasizing headings in both print and screen presentations using a paired comparison study to test which methods of typographic differentiation creates headings which are easiest to identify in text. This research found that headings with the greatest typographic weight are considered easiest to identify in both print and screen-based texts.

Information Design Journal
University of Waikato (NZ)
Quality Education
Openalex Percentile: Top 85%
Handwritten Text Recognition Techniques
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