AdaptiFont: Increasing Individuals’ Reading Speed with a Generative Font Model and Bayesian Optimization

Kadner, Florian; Keller, Yannik; Rothkopf, Constantin A.

doi:10.1145/3411764.3445140

Cited by 20 publications

(8 citation statements)

References 57 publications

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“…Ideally, if this were true, a personalized font recommender would not be necessary. Prior research agrees with this idea [47,93]. Our within-subjects study design allows us to compare FontMART's recommended font with the other seven fonts each participant reads in.…”

Section: One-size-fts-all Fontmentioning

confidence: 65%

“…Fonts are a vital element of modern digital reading interfaces. However, open research questions persist on the need to personalize font choice and how best to do so [47,73,80].…”

Section: Related Workmentioning

confidence: 99%

“…Recent work incorporated newer fonts designed for digital screens, such as Roboto and Open Sans [90]. Additionally, based on Kadner et al's recommendation to include typography experts in-the-loop [47], we collaborate with typographers to select fonts well-suited for digital reading to explore the efect of various font characteristics on reading performance.…”

Section: Multifactorial Infuences On Readingmentioning

confidence: 99%

See 2 more Smart Citations

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: One-size-fts-all Fontmentioning

confidence: 65%

“…Fonts are a vital element of modern digital reading interfaces. However, open research questions persist on the need to personalize font choice and how best to do so [47,73,80].…”

Section: Related Workmentioning

confidence: 99%

Section: Multifactorial Infuences On Readingmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Koyama et al [36,37] use a similar approach to allow users to rapidly adjust the visual appearance of an image in line with some desired aesthetic. Bayesian optimization has also been used as a tool to determine game mechanic settings to maximize engagement [33], adjust font parameters to maximize reading speed [31] and adjust interface and interaction features to minimize task completion time [15]. These various studies serve to highlight how Bayesian optimization provides an effective tool to support design tasks in HCI.…”

Section: Bayesian Optimizationmentioning

confidence: 99%

Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques

Chan

Liao

et al. 2022

CHI Conference on Human Factors in Computing Systems

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Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.

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“…This includes different writing scenarios such as story writing [8,107], email writing [12,57], and scientific writing [29]. Everyday work was the target in 12 publications (16.7%) such as font generation [40], video captioning [108], and conversation [35] which can be encountered in our everyday life. There were 12 publications (16.7%) targeted design, which captured various subdomains such as fashion design [70], furniture design [91], UI/UX design [3,25,54,66,97], and graphic design [90].…”

Section: Taskmentioning

confidence: 99%

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

Ko¹,

Park²,

Jeon³

et al. 2022

Preprint

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Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality opendomain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LT-GMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

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AdaptiFont: Increasing Individuals’ Reading Speed with a Generative Font Model and Bayesian Optimization

Cited by 20 publications

References 57 publications

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

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

Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

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