Let Me Choose: From Verbal Context to Font Selection (2020)

Abstract: In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.

## introduction in visual designs, textual information requires the use of fonts with different properties. whether it is books, magazines, flyers, ads or social media posts, different typefaces are commonly used to express non-verbal information and add more dimensions to the text. an appropriate font usually embodies information about character, context and usage of the design @xcite . this motivates us to explore font associations with regular users in a crowd-sourced setting. in other words, we investigate how users relate fonts to different characteristics of the input text. current font selection interfaces such as @xcite and commercial online services (e.g., myfonts foot_0 and typekit foot_1 ) assist users in selecting fonts by taking into account font similarity. however, they do not consider the verbal context of the input text. having a better understanding of the input text, users can benefit from a font recommendation system during authoring, saving time and avoiding tedious exploration of long lists of fonts. most graphic designers agree that there is no strict or universally-accepted rule for choosing fonts. different social and personal factors can be involved in typeface selection, which makes this process subjective. however, there seems to be enough agreement among human opinions to build reasonably effective models of font properties @xcite . several empirical studies have directly explored the relationship between fonts and texts @xcite @xcite . for example, brumberger (2003a) indicates that readers have strong opinions about the appropriateness of particular typefaces for particular text passages, and they can differentiate typeface/text mismatches. in this study, we aim to model for the first time the associations between visual font attributes and textual context, with the final goal of better font recommendation during text composition. our main contributions are: 1) we propose and formulate a new task: "font recommendation from written text." 2) we introduce a new dataset, short text font dataset, containing a variety of text examples annotated with ten different representative fonts. 3) we compare different end-to-end models that exploit contextual and emotional representations of the input text to recommend fonts. these models are able to capture inter-subjectivity among all annotations by learning label distributions during the training phase. we show that emotional representations can be successfully used to capture the underlying characteristics of sentences to suggest proper fonts. task subjectivity in some tasks, aggregated annotations always correspond to the correct answer @xcite . therefore, to fully utilize the crowd's knowledge, different approaches have been proposed to aggregate labels, from simply applying majority voting to more sophisticated strategies to assess annotators' reliability @xcite @xcite . all of these methods rely on the assumption that only one answer is correct and should be considered as ground truth @xcite . whereas in tasks like ours, sentiment analysis @xcite or facial expression @xcite ), the answer is likely to be more subjective due to its non-deterministic nature @xcite . we follow previous studies that successfully employed label distribution learning to handle ambiguity in the annotations @xcite @xcite . ## font dataset the proposed dataset includes 1,309 short text instances from adobe spark foot_2 . the dataset is a collection of publicly available sample texts created by different designers. it covers a variety of topics found in posters, flyers, motivational quotes and advertisements. 4 choice of fonts a vast number of fonts and typefaces are used in contemporary printed literature. to narrow down the task, we had a font expert select a set of 10 display fonts that cover a wide range of trending styles. these fonts display enough differentiation in visual attributes and typical use cases to cover the topics in our text samples. figure 1 shows several examples from the dataset, each rendered with the most congruent font (font with the highest agreement). annotation process in an mturk experiment, we asked nine annotators to label each sample text by selecting their top three fonts (figure 2 ). workers were asked to choose suitable fonts after reading the sentence. we included carefully-designed quality questions in 10 percent of the hits to monitor the quality of our labeling. we also needed to ensure workers selected fonts based on the comprehension of the text rather than just personal preference. therefore, we removed the annotations of workers who selected the same font more than 90 percent of the time, resulting in six to eight annotations per instance (we removed instances with fewer than six annotations). as we mentioned earlier, we asked annotators to rank their top three font choices for each text in our dataset. we decided to treat the first, second, and third choices differently as they represent the workers' priorities. therefore, we give the highest weight to the first choices (1.0) and lower weights (0.6) and (0.3) to the second and third choices, respectively. figure 3 shows three examples with label distributions over 10 fonts. by comparing the label distributions of these examples, we can observe that 'formal' fonts like f0, f2, and f5 are often selected in business contexts (left). 'modern/display' fonts like f1, f3, and f8 are favored in more casual settings (center), and 'script' fonts like figure 3: label distributions for three examples figure 4: average label distribution of the entire corpus f4, f8, and f9 are preferred for more emotional contexts (right). we observe that some fonts are more popular than others. figure 4 shows the average label distribution over all instances. f3, f2, and f1 are the most popular, while f4, f8, and f9 are the least popular among all 10 fonts. ## methodology task definition given a piece of text x, we want to determine which font(s) @xmath0, ...y 9 } are more appropriate or congruent with the properties of the input text. we formulate this problem as a ranking problem where the model assigns each font a real value d x y , representing the degree to which y describes x. in other words, d x y represents the degree of congruency of font y with input x. the values for all the labels are summed up to 1 to fully describe the instance @xcite . ## conclusion in this paper, we associated font with written text and tackle the problem of font recommendation from the input text. we collected more than 1,300 short written texts and annotated them with ten fonts. we formulated this task as a ranking problem and compared different models based on emotional and contextual representations that exploit label distribution learning to predict fonts. the current approach covers a fixed number of fonts, but it can be extended to support a larger set of fonts. for example, we can use font similarity techniques and enable users to pick a group of