CREATING CHARACTER CONNECTIONS FROM MANGA
Harumi Murakami, Ryota Kyogoku
Graduate School for Creative Cities, Osaka City University, Japanharumi@media.osaka-cu.ac.jp, kyoryo7@gmail.com

Hiroshi Ueda
Musium Media Division, ATR-Promotions, Inc., Kyoto, Japanh-ueda@atr-p.com

Keywords:

Character connections, Comics, Manga, Dragon Ball, Frames.

Abstract:

We presented a method to create character connections from manga using the frequencies of characters andtheir co-occurrences by referring to frames. First, we input characters and frames with a data input tool.
Second, we calculated the frequencies of characters and the relationships among characters and grouprelated characters. Third, we created character connections. Preliminary experiments using Dragon Ball vol.
32 suggest the usefulness of our approach.

1

INTRODUCTION

In recent years, the popularity of manga (Japanesecomics) has increased worldwide, and a large
amount of manga is being published. As of 2008, theU.S. and Canadian manga market generated $175
million in annual sales (Reid 2009).
Manga is usually first serialized in magazinesand later compiled in books. Some manga is a longepic. For example, Dragon Ball, a well-known
manga written and illustrated by Akira Toriyama,
was originally serialized from 1984 through 1995;
later its 509 individual chapters were published as
42 book volumes.
Since finding a chapter, a specific volume, or amanga itself from a large amount of mangacollections is difficult, we investigate how to find achapter or a particular manga volume.
Character connections are often created tointroduce the contents of multimedia such as movies,
anime, and TV dramas that help people understandthe characters and the complicated stories. We
believe that character connections are also useful forfinding and understanding manga. However,creating character connections from manga isunclear, time-consuming, and expensive.
This research creates character connections frommanga to help users find and understand its contents.
Below, in Section 2 we explain our approach’s
overview. Algorithms and preliminary experiments

are described in Sections 3 and 4. Related work isshown in Section 5.

2OUR APPROACH

This research creates character connections frommanga.
First, we input characters and frames with a datainput tool. Second, we calculate the frequencies ofcharacters and relationships among characters andgroup-related characters. Third, we create characterconnections.
The main feature of this research is creating
character connections from manga using thefrequencies of characters and their co-occurrencesby referring to frames. No current work createscharacter connections from manga or comics.

Goku

Cell

#16

#18
Tien
Shinhan Gohan Vegeta

TrunksKrillin

Mr. Popo

Piccolo

Bulma

Figure 1: Created character connections from Dragon Ball
vol.32.

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ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

Figure 1 shows created character connections
from an experiment using Dragon Ball vol.32.

3

Data input is time-consuming. We prepared a data
input tool using excel macro to reduce the burden of
the task. First, we manually extracted the characternames contained in the manga and input them into
the tool. Next, a user read the manga and pressed abutton for the registered character names when theyappeared. Finally a data file was produced. It takes
approximately one hour to input data from one
volume (about 130 pages).

#181#163

TienShinhan
4Cell

¥374

/* beginning ofchapter 374 */Cell/* character *////* frame */Android #16Android #18//
Tien Shinhan//
Cell/////* end of page */Cell

Data file

A manga page

Figure 2: Example of data input.

Figure 2 shows an example of creating a filefrom a page, beginning at chapter 374 of Dragon
Ball vol. 32. Manga flows from top to bottom andright to left. In the first frame of the first page, Cell(character name) appears. In the next frame, Android#16 (hereafter #16) and Android #18 (hereafter #18)
appear.

3.2

Judging Relations

3.2.1 Relations inside Frames
We assume that characters who appear in the same
frame are related. When more than one character
appears in a frame, we give related scores to eachcharacter and assume that when the number ofcharacters inside frames is smaller, the relation isstronger. Related scores inside frames between
characters Ci and Cj are calculated as:

678

(1)

3.2.2 Relations outside Frames

Data Input20.25 (if n  5)

Here n is the number of different characters from thedesignated character inside a frame.

ALGORITHMS

3.1

Rin(ci, cj)= 1 / n (if n  1,2,3,4)

We assume that characters who appear in the next
frame are related. When different characters appear
in subsequent frames, related scores are added toeach character. Related scores outside frames
between characters Ci and Cj are calculated as:Rout (ci, cj)= 0.5 / n (if n  1,2,3,4)
0.25(if n  5)(2)

Here n is the number of different characters from thedesignated character outside (next) the frame .

3.2.3 Unifying Relations
Finally, related scores between ci and cj arecalculated as the summation of the related scores ofrelations inside and outside frames, as shown inEquation (3).
R (ci, cj)=nn 1k 1

k 1

 Rin(ci, cj )   Rout (ci, cj )(3)Here n is the number of frames in designated units(e.g. chapters, volumes, etc.)
We created a table whose rows are characterssorted by their frequencies and whose columns arerelated characters sorted by their related scores.
Table 1: Five most frequent characters with five mostrelated characters.
Related characters
12
3
4
5
1
Cell
Ve
Tr
16Kr
18
[266]
172.5
43.1
24.0
23.7
15.7
2
VegetaCe
Tr
GoKr
16[196]
172.5
28.2
10.4
10.2
8.3
3
Krillin
Tr
Ce
18
Bu
16
52.6
43.1
20.1
14.0
11.4
[115]
3
Trunks
KrCeVe
Go
Bu
[115]
52.6
43.1
28.2
13.2
8.1
5
Goku
Gh
Ti
Ce
Tr
Ve
[69]
43.8
15.0
14.3
13.2
10.4
Note: [] indicates appearance frequency of characters; numbers onright hand are related scores.
Note 2: Ce:Cell; Ve: Vegeta; Kl: Krillin; Tr: Trunks;
Go: Goku; 18: #18; 16: #16; Gh: Gohan; Bu: Bulma;
Ti: Tien Shinhann; Pi: Piccolo; Po: Mr. PopoCharacter

Table 1 shows the five most frequent charactersand the five most related characters. For example,

CREATING CHARACTER CONNECTIONS FROM MANGA

Cell is the most frequent character and appears 266
times in vol. 32. The most related character to Cell isVegeta whose related score with Cell is 172.5.

3.3

Grouping

We group strongly related characters using the table
generated by the previous step. The groupingalgorithm is shown in Figure 3.
Step 1. We verify the differences between the related
scores in a raw and set the border using the biggest
difference. We make an initial group from the leftmost characters to the characters next to the border.
Step 2. We check all initial groups and merge thoseinclude same characters in them.
Figure 3: Grouping algorithm.

For example, for the first raw of Cell, 129.4(172.5-43.1) is the biggest among (172.5-43.1),(43.1-24.0), ..; Cell and Vegeta are grouped into an
initial group. Likewise, Vegeta and Cell are groupedfrom the second raw. These two initial groups aremerged into one group.
Table 2 shows the grouping results from Table 1.
The right column shows the evaluation value in ourexperiment.
Table 2: Grouping results.
GroupCell, Vegeta
Krillin, Trunks
Goku, Gohan
Tien Shinhan, Piccolo#18, #16
average

3.4

Evaluation
5.0
3.8
5.0
3.0
4.8
4.3

Generating Character Connections

We generated character connections using thefrequencies of characters, their related scores, andgroups. The algorithm is shown in Figure 4.
Step 1. Characters whose frequencies are in the top
three are displayed at the center.
Step 2. Characters whose frequencies are ranked
fourth or fifth are displayed alongside.
Step 3. When there are groups for all displayed
characters, the characters in identical groups are
displayed near the displayed characters.
Step 4. Characters who are not displayed in Step 1 to
3 are displayed alongside by ordering frequencies.
Step 5. Lines are drawn between characters whose
related scores are exceed 10.
Figure 4: Algorithm of generating character connections.

For example, Cell, Vegeta, Krillin, Trunks aredisplayed at the center in Step 1. Goku is displayed
at the top left in the Step 2. Gohan is displayed near
Goku in Step 3. After character connections aredisplayed, the position of some characters (e.g.
Krillin and Trunks) are moved by manually, as inFigure 2.

4EXPERIMENT

4.1

Overview

Dragon Ball vol.32 (a book with 126 pages and 12chapters) was used for the experiment. 12 characters
were identified. Five subjects (four male and one
female, aged 22-28) who have already read somevolumes of Dragon Ball participated in theexperiment.
We investigated (1) relatedness, (2) grouping,and (3) character connections.

4.2

Relatedness

The five subjects evaluated the relatedness between
12 characters by five values (5: very related; 4;related; 3: intermediate; 2: not very related; 1:unrelated).
From these results, relations whose related
values exceeded 3.6 were extracted and sorted. We
treat this table a correct data set. The first raw of thetable is shown in Table 3.
Table 3: Correct data set for related scores.
Ce1Ve

2183Go4Kl

5Tr

616Note: See Table 1 for abbreviations for characters.

We prepared the following comparative
methods: (a) calculated related values only using
inside frames (inside only) and (b) calculating
related values only using outside frames (outsideonly).
We created a related table (Table 4) for eachmethod. Characters ranked under 7 were omitted
because there was no correct answer. We counted
the rank of the correct dataset: smaller is better.
Table 4 shows the table for our method. For example,for the first raw Cell, 1 (Vegeta) + 5 (Trunks) + 6(#16) + 4 (Krillin) + 2 (#18) + 3 (Goku) = 21. The
totals for our method, inside only, and outside only
were 62, 66, and 63, respectively. Our method wasthe best.

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ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

Table 4: Dataset for our method.
CeVe
Kl
Tr
Go
1816GhBu
TiPi
Po

1Ve *
Ce *
Tr *
Kl *
Gh*
16 *
18 *
Gk*
Kl *
GoTi
Go

2
Tr *
Tr *
Ce *
Ce *
Ti
Kl *
Ce *
BuTrPiGoGh

316 *
Go*
18 *
Ve *
Ce *
Ce *
Kl
TrPiCeBu
Tr

4Kl *KlBu *
Go
Tr
VeVe
Ti
GoBuTrVe

518 *1616BuVe *Tr
TrPiGhGhGhPi

6Go*
18Ve
16PiTi
Ti
PoTi
TrVe
Ti

Note: *: correct

4.3

Grouping

Five subjects evaluated whether the generated
groups were appropriate by five values (5: veryappropriate; 4: appropriate; 3: intermediate; 2: notvery appropriate; 1: inappropriate).
The average value was 4.3 (See Table 2), and theresult shows the grouping algorithm is satisfactory.

4.4

Character Connections

Five subjects evaluated the created character
connections by five values as grouping. Finally, weconducted interviews about the above experiments.
The average evaluation value for the character
connection was 4.4. All subjects believed that Cell
was the most important character, although Goku is
the hero of the Dragon Ball. They felt that thepositions of Cell (center) and Goku (upper left) wereappropriate.
These results suggest that the created character
connections expressed the contents of the volume
well and the frequency of the characters indicatesthe importance.

5RELATED WORKNo previous research creates character connections
from manga or comics.
Some research create character connections fromother media. Goto et al. (2008) create character
charts from EPG texts that introduce movies. Bothstudies use natural language understanding
techniques to identify relations between characters.
They do not deal with manga or comics.
Spysee, whose algorithm is based on Matsuo etal. (2006) extracts person information from the Web

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and displays social networks. Some famous
character names were input as person names. For
example, Cell is connected not only with such othercharacters as Krillin but also a voice actor of Cell inanimation based on manga. This method is notadequate to express the character connections ofdesignated units such as chapters or volumes.
Ogasawara et al. (2008) extracted persons frombroadcast videos to construct a human correlationgraph and examined both text and image processing;
they didn’t create graphs.

6SUMMARY

We presented a method to create character
connections from manga using the frequencies ofcharacters and their co-occurrences by referring toframes. Preliminary experiments using Dragon Ball
vol. 32 suggest the usefulness of our approach. Since
this is merely the first step of our research, we needto improve our algorithms and conduct furtherexperiments using different manga. We believe ourapproach is applicable to other types of comics andshould be investigated in the future.

REFERENCES
Reid, C., 2009. Graphic Novel Sales Up 5%; Manga Off
17% , In Publishers Weekly.
Goto, J., Yagi, Y., Aizawa, A., Sekine, S., 2008.
Generation of Correlation Charts from TV programs
based on Anaphora Resolution. In The 22nd Annual
Conference of the Japanese Society for Artificial
Intelligence. (CD-ROM) (in Japanese).
Matsuo, Y., Mori, J., Hamasaki, M., Ishida, K., Nishimura,T., Takeda, H., Hashida, K., Ishiduka, M., 2006.
Polyphonet: An advanced social network extraction
system, In WWW2006, 397-406.
Ogasawara, T., Takahashi, T., Ide, I, Muase, H., 2005.
Construction of a Human Correlation Graph fromBroadcasted Video, In The 22nd Annual Conference ofthe Japanese Society for Artificial Intelligence. (CDROM) (in Japanese).