Digital typography

Rougier, Nicolas P.; Esfahbod, Behdad

doi:10.1145/3214834.3214837

Cited by 3 publications

(2 citation statements)

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“…Similar to typographers' views (P2, P4, P5, §3.2.2), our model found strong interaction efects between age and weight. In addition to the empirical validation provided by the model, the typographers and past research confrm that the anatomy of Georgia is designed for digital reading (P1, [58]) and that serifs may help older participants more easily distinguish characters [1,59,76]. Our recommendation for this age group may be an overgeneralization because of the skewed age distribution of the participants.…”

Section: Fontmentioning

confidence: 88%

“…Our methods to measure font familiarity may be sub-optimal, thus explaining its inconsistent effect on reading speed in our results ( §6.2.2). It relies on participants' varying abilities to recognize fonts and judge their familiarity based on examples [1,59,62,76]. Future research could develop more reliable methods to measure font familiarity over time and account for participants' prior habits and experiences with fonts.…”

Section: Self-assessed Reader Characteristicsmentioning

confidence: 99%

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Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Cai

Wallace

Rezvanian

et al. 2022

Designing Interactive Systems Conference

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The amount of text people need to read and understand grows daily. Software defaults, designers, or publishers often choose the fonts people read in. However, matching individuals with a faster font could help them cope with information overload. We collaborated with typographers to (1) select eight fonts designed for digital reading to systematically compare their efectiveness and to (2) understand how font and reader characteristics afect reading speed. We collected font preferences, reading speeds, and characteristics from 252 crowdsourced participants in a remote readability study. We use font and reader characteristics to train FontMART, a learning to rank model that automatically orders a set of eight fonts per participant by predicted reading speed. FontMART's fastest font prediction shows an average increase of 14-25 WPM compared to other font defaults, without hindering comprehension. This encouraging evidence provides motivation for adding our personalized font recommendation to future interactive systems.

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Section: Fontmentioning

confidence: 88%

Section: Self-assessed Reader Characteristicsmentioning

confidence: 99%

Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Cai

Wallace

Rezvanian

et al. 2022

Designing Interactive Systems Conference

View full text Add to dashboard Cite

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Real‐Time Analytic Antialiased Text for 3‐D Environments

Ellis

Hunt

Hart

2019

Computer Graphics Forum

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Text is a crucial component of 3‐D environments and virtual worlds for user interfaces and wayfinding. Implementing text using standard antialiased texture mapping leads to blurry and illegible writing which hinders usability and navigation. While super‐sampling removes some of these artifacts, distracting artifacts can still impede legibility, especially for recent high‐resolution head‐mounted displays. We propose an analytic antialiasing technique that efficiently computes the coverage of text glyphs, over pixel footprints, designed to run at real‐time rates. It decomposes glyphs into piecewise‐biquadratics and trapezoids that can be quickly area‐integrated over a pixel footprint to provide crisp legible antialiased text, even when mapped onto an arbitrary surface in a 3‐D virtual environment.

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Use of the “quick brown fox jumps over the lazy dog” pangram in academic papers

Teixeira da Silva

2024

Journal of Electrical Systems and Inf Technol

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In the English language, when a sentence contains all letters of the alphabet, such as “The/A quick brown fox jumps over the lazy dog”, this is known as a pangram. Curiously, despite its odd meaning, this fox-dog pangram has found practical usage in some applications, mainly in text typography related to computer graphics, in linguistics, as a writing tutorial, or for testing computer keyboards. Even more curiously, it has been detected as a residual of template text in some academic papers. This unique linguistic curiosity was examined by screening Scopus and Web of Science, as well as Google Scholar from 2019–2023, to appreciate its practical use, as well as to assess whether there were any cases of residual template text, the main target of this exercise. In the latter case, this letter advocates for the removal of this residual text, in the form of a literary correction.

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Digital typography

Cited by 3 publications

References 0 publications

Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

Real‐Time Analytic Antialiased Text for 3‐D Environments

Use of the “quick brown fox jumps over the lazy dog” pangram in academic papers

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