Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements
Yuto Shinahara∗ , Takuro Karamatsu∗ , Daisuke Harada∗ , Kota Yamaguchi† and Seiichi Uchida∗∗ Kyushu University, Japan† CyberAgent, Inc., Japan

Abstract—In this paper, we conduct a large-scale studyof font statistics in book covers and online advertisements.
Through the statistical study, we try to understand how graphic
designers relate fonts and content genres and identify the relationship between font styles, colors, and genres. We propose an
automatic approach to extract font information from graphic
designs by applying a sequence of character detection, styleclassification, and clustering techniques to the graphic designs.
The extracted font information is accumulated together withgenre information, such as ‘romance’ or ‘business’, for further
trend analysis. Through our unique empirical study, we showthat the collected font statistics reveal interesting trends interms of how typographic design represents the impression
and the atmosphere of the content genres.
Keywords-font statistics; visual design analytics; book cover
design; online advertisement design;

I. I NTRODUCTION
Various visual designs, such as poster, book cover, advertisement, or product packaging, contain textual information
printed in various fonts. Fig. 1 shows examples of bookcover and online advertisement graphics. Why is a certain
text printed with a specific font? For example, engineering
documents might have a bold sans-serif title. This question
can be rephrased as follows: how typographers to choose afont for the book? We can imagine that there is no strict anduniversal rule to visual designs, and the creation process caninvolve various social and psychological factors of individual
designers. However, still there might exist general principles
or tacit rules by designers that we could observe as trends
in a large volume of visual design examples.
The purpose of this paper is to study the statistical trendsof font usage in visual designs. Using two different large
design datasets, i.e., book covers and online advertisements,
we aim at undermining hidden trends in font use in graphic
designs via computational approach. For analyzing fontusages in book titles, we automatically extract a title partfrom book covers using scene text detector, and identify its
font style (e.g., Sans-serif) and font color. Our empirical
study from 207,572 book covers reveals unique trends infont styles and colors for different book genres, such as
strong preference of decorative fonts in recreational books.
Our cluster analysis suggests there are clusters of similar
book genres in terms of font use, which often reflects theproximity of the topic in terms of content. Our second study
using 30,000 advertising banners also suggests there exist
unique trends of font usage at different business category.
Our automated analysis successfully discover hidden rules

(a)

(b)

Figure 1: Examples of a) book covers and b) online advertisements. We propose to automatically extract, categorize,
and analyze font information from these graphic designs.

or common sense that typographers intentionally or unintentionally use.
To the best of authors’ knowledge, our study is one of thefirst attempts in quantitative and automatic font analysis. Our
approach builds upon recent methods of scene text detection
and recognition techniques [1], [2], and well demonstrates
how a computational approach enables a systematic scientific study at scale that has been impossible in the past.
There has been psychological insights that graphic designers
or typographers choose fonts based on impression andatmosphere of the content [3]–[6], and our result provides
quantitative evidence for existing hypotheses or verbalizes
tacit rules in design. From an engineering perspective,
our statistical analysis enables development of interesting
applications such as automatic font recommendation, fontgeneration, or typography evaluation.
Our contributions are summarized in the following:• We propose a novel attempt in visual font analysis using
recent text recognition techniques; and• We conduct a large-scale empirical study using realworld datasets of 207,572 book covers and 30,000
online advertising, and show there indeed exist specificpatterns in font usage in visual design.
II. R ELATED WORK
A. Design and Psychology Literature
In design research, font usage has been dealt mostly as
subjective knowledge of typographers. While there are many
typography guides and hints for appropriate font usage, they
are rather intuitive suggestions and merely supported by any
quantitative fact derived from data analysis. On the other
hand, visual design analysis, such as advertisements [7],

input:book cover image

text
detection

text
recognition

title extraction(with edit distance)

font color
estimation

title font color
R100G066B117“Personalitv”
binarization“A/a”“CLASSIC”“P/p”

segmentation“Z/z”“Schustaok”

metadata: title = “Personality”
genre = “Health, Fitness & Dieting”

pss“ P e r s o n a lity ”

font styleestimation

Fancy
Historical
ScriptHybridSans Serif
Serif

…

repeat the voting
for all charactersthen take average

title font style

Figure 2: Our pipeline for font style and color extraction from book titles.
[8] or infographics [9], is now a trend in computer visioncommunity. However, font usage in those visual designs has
not been analyzed even in the trend.
In psychology literature, perceptual impression of fonts
has been a research topic from 1920’s [10], [11]. Following
those pioneering studies, many modern psychological studies
have revealed the relationship between font style and impression [3]–[6], and still the topic is actively studied [12]–[14].
Those studies reveal impression of fonts through subjective
human experiments with questionnaire answering. Amongthem, O’Donovan et al. [13] creates a notable font dataset
where 37 attributes, such as wide, complex, and friendly, areattached to the individual fonts.
In contrast to design and psychological literature, we takea computational approach to analyzing fonts from large collections of graphic designs. Our results are totally objective
and reproducible. In addition, the results are reliable enoughbecause they are derived from 207,572 book covers and
30,000 advertisement images.
B. Computer Science Literature
Font generation and font recognition has recently become
a hot research topic in computer science. For font generation,a subspace representation [15], a pairwise interpolation [16],and a patch-based optimization [17] are proposed. Recently,
many generative adversarial networks (GAN)-based fontgeneration methods (e.g., [18]–[20]) are proposed and applied even to huge-category tasks, such as Chinese fontgeneration. For font recognition, CNN-based methods [21],[22] are proposed and showing accurate performance. For
example, DeepFont [21] achieves 80% as top-5 accuracy on
a 617 font-class task.
Recently, there are a couple of advanced attempts forgenerating or choosing an appropriate font to the input
image. Zhao et al. [23] propose a method that generates
a font while treating a given Web-page image as a visualcontext. It also estimates appropriate color and size forshowing the font. Choi et al. [24] proposed a method thatchooses a font while considering the atmosphere of the input
scenery image.
In addition to those font studies, scene text detection
and recognition methods are intensively studied so far.
Recent methods based on deep neural networks can detect
and recognize scene texts even if they are printed withheavily-decorated fonts. Those robust scene text detectors
and recognizers are mandatory to extract “fonts in the wild”
before their analysis. Among the recent methods, EAST [1]and CRNN [2] becomes one of the standard methods thanks
to their ground-breaking performance on scene text detection
and recognition, respectively. In this paper, we also use
EAST and CRNN for extracting fonts used for book titles
from book cover images, because they showed sufficientperformance for our task.
We emphasize that quantitative analysis of font usage isan novel direction of research in the document analysis andrecognition community. There is not yet a constituted stateof-the-art. In fact, it was almost impossible so far to collectmany font usages without recent scene text detection andrecognition methods introduced above.
III. F ONT A NALYSIS IN B OOK T ITLES
A. Book Cover Dataset
We collected 207,572 book cover images from Amazon.
com. The image dataset is published at this URL 1 . The
dataset consists of a collection of cover images and associated metadata such as a title or a genre. Fig. 3 (a)lists 32 book genres defined by Amazon.com along with
the number of the cover images at each genre. Each bookbelongs to a single genre and the number of books varies
according to genre.
B. Font Style and Color Extraction
Fig. 2 shows our pipeline for identifying the title font froma book cover image. The steps are divided into three parts;
1 https://github.com/uchidalab/book-dataset

representative colors
R166G066B084

R191G056B043

R095G040B031

R220G205B195

R161G121B082

R222G161B073

R230G220B071

R217G210B147

R064G064B054

R137G138B135

R079G133B075

R117G167B194

R057G078B114

R062G047B116

R023G021B021

Other 14 colors

R239G243B242

Fancy
Historical Script
ScriptHybridSans-serif
Serif

font styleclassesaverage frequency
genre
Arts & Photography
Biographies & Memoirs
Business & Money
CalendarsChildren's Books
Christian Books & Bibles
Comics & Graphic Novels
Computers & Technology
Cookbooks, Food & Wine
Crafts, Hobbies & Home
Education & Teaching
Engineering & Transportation
Gay & Lesbian
Health, Fitness & Dieting
History
Humor & Entertainment
Law
Literature & Fiction
Medical Books
Mystery, Thriller & Suspense
Parenting & Relationships
Politics & Social Sciences
Reference
Religion & Spirituality
Romance
Science & Math
Science Fiction & Fantasy
Self-HelpSports & Outdoors
Teen & Young Adult
Test Preparation
Travel#imagesgenre-wise difference from the average usage

6,460
4,261
9,965
2,636
13,605
9,139
3,026
7,979
8,802
9,934
1,664
2,672
1,339
11,886
6,807
6,896
7,314
7,580
12,089
1,998
2,523
3,402
3,268
7,559
4,291
9,276
3,800
2,703
5,968
7,489
2,906
18,338Serif

Sans Serif

HybridScriptHistorical Script

Fancy
10%

(a)

40%

10%

70%

(b)

0%

(c)

18%

(d)

Figure 3: Summary and visualization of font-genre relationships in book covers: (a) 32 book genres and their associatednumber of images, (b) style usage, (c) color usage, and (d) font style examples.

title text extraction, title font color estimation, and title fontstyle estimation.
1) Title Text Extraction: As the first step for title text
extraction, we apply EAST [1] to detect all words asbounding boxes from the book cover image. EAST is robust
to non-horizontal layouts, font decoration, size variations,and color variations, and thus can accurately detect mostwords. Next, we use CRNN [2] to recognize each detected
word. CRNN can be a lexicon-free word recognizer andthus has a potential to recognize even proper nouns andrare words. Finally, the bounding box of the title text is
extracted from the recognized words using metadata of thebook title. Edit distance is used here to identify correct words
because there might be some character recognition errors
from CRNN. We extract only title fonts and discards otherirrelevant texts such as author names.
2) Font Style Estimation: We estimate font styles atindividual characters in the extracted title text. Instead of
estimating the exact font name (such as “Helvetica”), we
estimate style category of the font. This is because an
accurate font recognizer, such as DeepFont [21], is not
publicly available and many rare fonts are used in the booktitles. In our setup, we categorize fonts into one of six fontstyles: 1) Serif, 2) Sans-serif, 3) Hybrid (mixture of serifand sans-serif), 4) Script, 5) Historical Script, and 6) Fancy,
according to [25], where 1,132 Adobe fonts are classified
into these styles. We show font examples in Fig. 3 (d).
A detailed procedure for font style estimation is explainedin the following. Let us assume we are given that the title
is “Personality” by metadata. First the bounding box of
the word image “Personality” is partitioned into individual
character components by the Otsu’s binarization. The resulting 11 character components are labeled as ‘P’, ‘e’, ‘r’,‘s’, and so on. Then the image patch of the first character‘P’ is compared with all 1,132 font images of ‘P’ and its
K nearest-neighbor font images are selected for each styles. The probability that theis printed in the
PKcharacter‘P’ P
6style s is given by ps = k=1 (1/ds,k )/( s0 =1 (1/ds0 ,k )),where ds,k is the pseudo-Hamming distance [16] to the kth nearest-neighbor font image of class s. The probability
vector p = (p1 , . . . , ps , . . . , p6 )T is averaged over all 11
character components of the title and then treated as the fontstyle usage of the book. In the experiment, we set K = 3
because there is no significant difference with a larger K.
3) Font Color Estimation: We estimate font color by separating the character strokes from their background within
the bounding box. We separate stroke regions by Otsu’sbinarization. Next, we average L∗ a∗ b∗ colors of all foreground pixels and treat this as the font color. Although
averaging is not appropriate when the title text has multiple
colors, we find such a case is rare and almost negligible
in our dataset. The averaged color is finally quantized into
one of the representative colors in the dataset. We use 16
representative colors (Fig. 3 (c)) determined by k-means on
all 207,572 L∗ a∗ b∗ color vectors.
C. Results
1) Font Styles: Fig. 3 (b) shows the frequency of fontstyles of all 32 genres as a bar chart and that of each genre
as a heat map. The heat map of a genre is the average ofthe probability vectors p over all book titles of the genre.
Consequently, the sum of the heat map values of each genre
is always normalized to 100%. We identify the following
trends:• Sans-serif fonts are the majority at all book genres. Especially, those fonts are very frequently used at genresfor practical books (how-to books), such as “Business& Money”, “Computers & Technology”, “Engineering& Transportation”, and “Test Preparation.” In otherwords, these genres use Serif fonts much less frequently
than other genres.
• Serif and historical script are used in genres abouthumanity, such as “Literature & Fiction”, “Religion &Spirituality”, and “Christian Books & Bibles.”• Fancy fonts (i.e., decorative fonts) are not dominant but
found at genres of recreational books, such as “Comics& Graphic Novels”, “Mystery, Thriller & Suspense”,
and “Science Fiction & Fantasy.”

Figure 4: Proximity of book genres by font styles. Genres about practice, recreation, and humanity form clusters
around red, blue, and green circles respectively.

Script and fancy appear as frequent as most other genres, although script and historical script have different
trends. This suggests that script fonts are treated as
decorated fonts rather than classical fonts.
2) Font Colors: Fig. 3 (c) shows the frequency of fontcolors of all 32 genres as a bar chart and that of each genre
as a heat map and reveals the following trends:• Achromatic colors (white, black, gray) and pale colors
are the majority in overall.
• Vivid colors become dominant in some cases, such as
red in “Children’s Books” and yellow in engineeringrelated genres (“Computers & Technology” and “Engineering & Transportation”).
• Dark chromatic colors (blue, green) are not common,
except for the usage of dark blue in genres relating
to study (“Parenting & Relationship”, “Education &Teaching”, “Test Preparation” and “Children’s Books”)
and health (“Health, Fitness & Dieting” and “SelfHelp”).
• White is often used in genres for practical books.
• Pale pink is often used in genres for humanity andrecreation books, such as “Romance”.
•

D. Discussion
1) Do Similar Genres Share Similar Font Styles?: Fromthe empirical observation, one may expect that similar
genres have similar font style usages. This expectation is
supported positively by Fig. 4. We show in Fig. 4 a twodimensional visualization of the genre proximity, which we
obtain by applying multi-dimensional scaling (MDS) to thesix-dimensional font style frequency vectors of all 32 genres.

R243G076B132

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average frequency

genre-wise difference from the average usage

0%

(a)

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12
15
20
29
26
41
57

Other 6 colors

12 1,363
8 3,385
16 3,025
17 2,953
23 2,210
21 9,374
33 5,788

R014G011B013#products

Business and Finance
Careers
Content Channel
Healthy Living
Personal FinanceStyle & Fashion
Video Gaming

representative colors
R253G253B252

genre#companies

Decorative
Calligraphic
Sans-serif-RoundSans-serif-Modern
Sans-serif-Retro
Sans-serif-Basic
Serif-Modern
Serif-Retro
Serif-Basic

font styleclasses#adv images

As suggested by circles, similar genres get close to eachother; for example, genres for practical books forms a clusterof the red circle. These results prove that there are clear
trends in font style usages for each genre, and typographers
purposefully choose an appropriate font for a book title
under this trends. We emphasize that those findings arise
from our automated analysis of 207,572 book covers. Notethat we also had a similar genre distribution with font color
usage, although it is omitted due to space constraints.
2) What Does It Mean to Be an “Average” Genre?:
According to the property of MDS-based visualization,
we can say that genres around the peripheral part of thetwo-dimensional genre distribution of Fig. 4 have a very
unique usage of font styles. Examples include: “Comics andGraphic Novels”, “Test Preparation”, “Religion & Spirituality” and “Christian Books & Bibles.” In contrast, genres
around the center of the distribution often implies that thosegenres have a very large variation and thus does not haveany focus on a specific font style. For example, fine arts
have various styles and subjects. Therefore, book titles in
the genre “Arts & Photography” are printed in various fonts
and therefore the genre goes to the center of the distribution.

27%

0%

(b)

57%

1%

19%

(c)

Figure 5: Summary and visualization of font-business relationships in advertisements: a) seven genres and theirassociated number of images, b) style usage, and c) color
usage.

C. Results
1) Font Styles: From Fig. 5 (b), we find:Sans-serif is majority in all genres. Especially, almost
all texts are printed in Sans-serif in financial genres:“Business and Finance” and “Personal Finance.”.
• Serif is preferred in “Style & Fashion”, “Video Gaming” and “Healthy Living.” Especially, “Style & Fashion” is the genre with the least sans-serif frequency.
• Calligraphy (brush-style) is frequently used in “Gaming,” where we find many games on historical matters.
•

IV. F ONT A NALYSIS IN O NLINE A DVERTISEMENTS
A. Online Advertisement Dataset
As another example to analyze genre-wise font usage,
we collected 28,098 online advertisement images from ourindustrial partner in Japan. Fig. 5 (a) shows their sevenbusiness genres we pick from IAB categories [26] andthe associated sample size. There can be multiple graphic
designs for the same product. The column “#products”
shows the number of products for each genre. On average,
we observe 28, 098/200 ∼ 140 designs per product. Fig. 5(a) also shows the number of companies for each genre.
B. Font Style and Color Extraction
The ads have two different properties from book covers.
First, we can get the precise font name (like “Century
Gothic”) and their color by inspecting a vector file format(Adobe Photoshop format). This property makes the analysis
very simple; we need to perform neither text detection nor
font style estimation. Second, there is no “title” in ads. We
instead need to analyze all the texts in the banner graphics.
In ads, texts appear in various sizes. We apply weighting foreach text proportional to its size in the analysis.
We classify fonts into one of the nine categories according
to a typographer guide [27]. We discard unclear fonts that are
not listed in the guide or embedded as a bitmap in the file.
For colors, we also prepare eight representative colors by
the procedure of III-B. The font styles and the representative
colors are shown in Fig.5. The frequency of font styles areaveraged per product and then per genre.

2) Font Colors: Fig. 5 (c) reveals the following findings:
Warmer and/or bright colors are majority in all genres. Especially, red appears in “Personal Finance” and“Content Channel” and brown in “Style & Fashion”
and “Careers.”• Blue colors are rarely used except that dark blue
appears very specifically in “Healthy Living”.
• Interestingly, pink does not correlate with red despite
their similarity in hue channel. Red is used in “Personal Finance” and “Content Channel” as a means ofemphasis, whereas pink is used to create an atmospherein “Style & Fashion” that often targets at women.
•

D. Discussion
The results clearly suggest that there exist a correlation
between fonts and business genres. Compared to bookcovers, however, online ads have some differences. A good
example is that the ratio of Serif in advertisement images
are lower than that in book titles. One possible reason is thatthe resolution of online ads is often low and thus the thinnerstroke structure of Serif fonts may damage the visibility. Our
analysis not only visualizes the genre and style correlation,
but also effectively reveals the differences in media formats.

V. C ONCLUSION
In this paper, we quantitatively analyze font usages usinglarge collections of book titles and online advertisements.
Our approach successfully identifies interesting trends interms of how typographic design represents the impression
and the atmosphere of the genres. Our cluster analysis using
multi-dimensional scaling visualizes that books under close
genres share similar font usages; for example, genres forpractical books use more sans-serif fonts for their title, while
genres for humanity also use serif fonts. Similar trends
are found in online advertisements. The discovered trends
suggest that typographers carefully choose font style andcolor for individual targets such that fonts appropriately
represent the content.
Visual font analytics is a novel research direction andhas many interesting open problems. In this paper, we only
observe the “average” usage of each genre; this means that
we did not observe the deviation from the average. The
deviation is important because visual designs often try toappeal some unexpectedness to impress their audiences.
From marketing science perspective, it is interesting toinvestigate how fonts and sales correlates, or how fonts affectconsumer behavior [28] in the future. Trends in multiple
font combination, text size and position, and visual effects
are also open problems for future data-driven typographic
study.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number JP17H06100.
R EFERENCES[1] X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and
J. Liang, “EAST: An Efficient and Accurate Scene Text
Detector,” Proc. CVPR, 2017.
[2] B. Shi, X. Bai, and C. Yao, “An End-to-End Trainable
Neural Network for Image-Based Sequence Recognition and
Its Application to Scene Text Recognition,” TPAMI, vol. 39,
no. 11, pp. 2298-2304, 2017.
[3] E. Brumberger, “The Rhetoric of Typography: The Awarenessand Impact of Typeface Appropriateness,” Technical Communication, vol. 50, no. 2, pp. 224-231, 2003.
[4] P. W. Henderson, J. L. Giese, and J. A. Cote, “Impression
Management using Typeface Design,” Journal of Marketing,vol. 68, no. 4, pp. 60-72, 2004.
[5] J. R. Doyle and P. A. Bottomley, “Dressed for the Occasion: Font-Product Congruity in the Perception of Logotype,”
Journal of Consumer Psychology, vol. 16, no. 2, pp. 112-123,
2006.
[6] J. Mackiewicz, “Audience Perceptions of Fonts in Projected
PowerPoint Text Slides,” Proc. IEEE Int. Professional Communication Conf., pp. 68-76, 2006.
[7] Z. Hussain, M. Zhang, X. Zhang, K. Ye, C. Thomas, Z. Agha,
N. Ong, and A. Kovashka, “Automatic Understanding of
Image and Video Advertisements,” Proc. CVPR, 2017.

[8] K. Ye and A. Kovashka, “ADVISE: Symbolism and External Knowledge for Decoding Advertisements,” Proc. ECCV,
2018.
[9] Z. Bylinskii, S. Alsheikh, S. Madan, A. Recasens, K. Zhong,
H. Pfister, D. Hanspeter, F. Durand, and A. Oliva, “Understanding Infographics through Textual and Visual Tag
Prediction,” arXiv, 2017.
[10] A. T. Poffenberger and R. B. Franken, “A Study of the Appropriateness of Type Faces,” Journal of Applied Psychology,
vol. 7, no. 4, pp. 312-329, 1923.
[11] R. C. Davis and H. J. Smith, “Determinants of Feeling Tone
in Type Faces,” Journal of Applied Psychology, vol. 17, no. 6,
pp. 742-764, 1933.
[12] B. Grohmann, J. L. Giese, and I. D. Parkman, ‘ “Using Type
Font Characteristics to Communicate Brand Personality of
New Brands,” Journal of Brand Management, vol. 20, no. 5,
pp. 389-403, 2013.
[13] P. ODonovan, J. Libeks, A. Agarwala, and A. Hertzmann,“Exploratory Font Selection Using Crowdsourced Attributes,”
ACM Trans. Graphics, vol. 33, no. 4, pp. 1-9, 2014.
[14] C. Velasco, A. T.Woods, S. Hyndman, and C. Spence, “The
Taste of Typeface,” i-Perception, vol. 6, no. 4, pp. 1-10, 2015.
[15] N. D. F. Campbell and J. Kautz, “Learning a Manifold ofFonts,” ACM Trans. Graphics, vol. 33, no. 4, pp. 1-11, 2014.
[16] S. Uchida, Y. Egashira, and K. Sato, “Exploring the Worldof Fonts for Discovering the Most Standard Fonts and theMissing Fonts,” Proc. ICDAR, 2015.
[17] S. Yang, J. Liu, Z. Lian, and Z. Guo, “Awesome Typography:
Statistics-Based Text Effects Transfer,” Proc. CVPR, 2017.
[18] S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman,
and T. Darrell, “Multicontent GAN for Few-Shot Font Style
Transfer,” Proc. CVPR, 2018.
[19] Y. Guo, Z. Lian, Y. Tang, and J. Xiao, “Creating New
Chinese Fonts based on Manifold Learning and Adversarial
Networks,” Proc. Eurographics, 2018.
[20] Y. Jiang, Z. Lian, Y. Tang, and J. Xiao, “DCFont: An
End-To-End Deep Chinese Font Generation System,” Proc.
SIGGRAPH Asia, 2017.
[21] Z. Wang, J. Yang, H. Jin, E. Shechtman, A. Agarwala,J. Brandt, and T. S. Huang, “DeepFont: Identify Your Font
from An Image,” Proc. ACM MM, 2015.
[22] Y. Wang, Z. Lian, Y. Tang, and J. Xia, “Font Recognition in
Natural Images via Transfer Learning,” Proc. MMM, 2018.
[23] N. Zhao, Y. Cao, R. W. Lau, “Modeling Fonts in Context:
Font Prediction on Web Designs,” Computer Graphics Forum,
vol. 37, no. 7, pp. 385-395, 2018[24] S. Choi, K. Aizawa, and N. Sebe, “FontMatcher: Font Image
Paring for Harmonious Digital Graphic Design,” Proc. IUI,
2018.
[25] Seibundo Shinkosha Publishing, Type Identifier for Beginners,
978-4-416-11346-2, 2013.
[26] K. Flood and N. Agnew, “IAB Tech Lab Content Taxonomy,” https://www.iab.com/guidelines/iab-quality- assuranceguidelines-qag-taxonomy/, 2017.
[27] MdN Corporation, Zettai Font Book, 978-4-844-36820-5,
2018.
[28] E. Buechel and C. Townsend, “Buying Beauty for the Long
Run: (Mis) Predicting Liking of Product Aesthetics,” Journalof Consumer Research, vol. 45, no. 2, pp. 275–297, 2018.