Semi-automatic Text and Graphics Extraction ofManga using Eye Tracking Information
Christophe Rigaud, Thanh-Nam Le, J.-C. Burie, J.-M. Ogier

Shoya Ishimaru, Motoi Iwata, Koichi Kise

Laboratoire L3i,
Université de La Rochelle
Avenue Michel Crépeau, 17042 La Rochelle, France{thanh nam.le, christophe.rigaud, jcburie, jmogier}@univ-lr.fr

Osaka Prefecture University
1-1 Gakuencho, Nakaku, Sakai, Osaka 599-8531, Japanishimaru@m.cs.osakafu-u.ac.jp,{iwata, kise}@cs.osakafu-u.ac.jp

Abstract—The popularity of storing, distributing and reading
comic books electronically has made the task of comics analysis
an interesting research problem. Different work have been carried out aiming at understanding their layout structure and thegraphic content. However the results are still far from universally
applicable, largely due to the huge variety in expression styles
and page arrangement, especially in manga (Japanese comics).
In this paper, we propose a comic image analysis approach using
eye-tracking data recorded during manga reading sessions. As
humans are extremely capable of interpreting the structured
drawing content, and show different reading behaviors basedon the nature of the content, their eye movements follow
distinguishable patterns over text or graphic regions. Therefore,eye gaze data can add rich information to the understanding of
the manga content. Experimental results show that the fixations
and saccades indeed form consistent patterns among readers, andcan be used for manga textual and graphical analysis.

I. I NTRODUCTION
Eye movements are generally considered by research communities as an important indication of human visual attention
during their interaction with media such as images or videos.
Early studies on recording and analyzing human eye movements date back to decades ago, and originally most attempts
were to improve the accuracy of eye-tracking devices, andto invent less intrusive methods [1]. Nowadays, eye-tracking
technique has reach a quite mature state where one can easily
develop a system on his own, and thanks to its ubiquitous
applications, a lot of manufacturers have developed a wide
range of consumer products in industrial scale. These devices
are getting more and more affordable and will probably be
integrated to a large number of reading devices, in order tocapture eye movements and provide additional functions.
Since eye-tracking data can be analyzed to provide manyfascinating insights about reading activity, such as in [2]–[4],it can be applied to understand the behavior of readers on
comic books reading. Manga (Japanese comic art) has long
become popular world-wide and represent a cultural trait aswell as an important industry. Manga, or comics in general,is the art of telling stories through a sequence of arranged
pictures (panels). The panels normally consist of text andgraphic elements: backgrounds, characters, speech balloons,visual effects, drawn texts, etc. The graphic is mainly in black
and white and gray shades depicted by halftoning dots.

Fig. 1. Example of a heat map on average saccade path of 25 readers. Blueto orange hues (lower to higher temperature) correspond to rare to frequent
paths taken by the readers. Manga content credits: [6].

Manga is often composed in a much more realistic andfree style compared to Western comics, and is challenging
to create. Besides immense drawing skill, it requires theexperience to compose the layout of elements, to effectively
convey the story and “secure control of the readers attention
and dictate the sequence in which the reader will follow
the narrative” [5]. Unlike motion picture, the story line ispresented spatially rather than temporally. Each single panel
is a frozen moment, carefully captured and set at its “right”
place. Manga artists control the flow of reader’s attention via
placement of elements, to lead the reader smoothly throughthe pages (e.g. the speech balloons can be easily associatedwith its corresponding subjects and read in the correct order).
The general rule to follow the pages in manga is to read fromright to left, from top to bottom, line by line, following the“reverse-Z” shape. However in practice, it is not always thecase. The configuration of the page, the size and the shape of
the panels can be so different from a certain page to the next
one, and some decisions are purely artistic choice, although
there are certain conventions in manga design.
With such a complex and rather free-style layout arrangement, the available methods in analyzing manga page content
still prove to be inefficient in various cases. Our work in thispaper is based on the premise that eye-tracking the readers
yield much more semantic information than the “bottomup” methods alone can provide. One contribution is that theadditional information allows “online” analysis of manga inparticular, which embraces top-down underlying semantic cues
(Fig. 1). In this study, we perform eye-tracking on different
readers on a set of manga images. All readers have some basic
background of reading manga, so the flow from panel to panel
is expected to be consistent. The eye-tracking patterns showthat the first hint is to actually follow is the speech balloon
arrangement, then the right-left, top-down rule. The viewers’
attention is indeed guided to follow a certain flow, not onlyin pages but also inside each panel. This offers information
to semi-automatically order panels or speech balloons. We
also propose to highlight the benefits of eye gaze position for
extracting elements from manga page images, as well as investigate the challenging problem of manga character retrieval
by analyzing the fixations and saccades. Another contribution
is the potential application of knowledge-driven analysis ofmanga content, where algorithms that can compute top-down
semantic cues via eye gaze data, can interact with bottomup information to infer new information, and to understandprogressively the content of the page, or support manga artists
in designing task.
In the next section, we review content extraction method
related to manga image analysis along with eye gaze information. Section III details the proposed system from eye
gaze movement acquisition to graphics retrieval. Section IV
presents the experiments that we performed to validate theproposed method. Finally, Section V and VI discuss andconclude this work respectively.
II. R ELATED W ORK
Manga analysis concerns several fields of research, fromlayout analysis to text extraction, graphics recognition and also
text/graphics association. We first review eye gaze information
for document analysis, and then address text and manga
character extraction as they are the most important informationin such images.
A. Eye gaze information
Eye gaze information is widely used for recognizing human’s activity including cognitive tasks like reading [7] [8].
In the field of document analysis, eye gaze is one of the best
features to investigate the content of document from the viewof human’s reading behavior. For example, important parts of
document for many readers can be distinguished by readingskimming detection proposed by Biedert et al. [9]. The total
number of words and words per minute of a certain reader andthe difficult to understand words can be also detected by using
eye-tracking devices [2], [3]. Although lots of work investigate
eye gaze with reading documents, there are few researches
which involve manga images. Kunze et al. have proposed
to classify five document types (novel, manga, magazine,
newspaper, textbook) by using eye gaze from mobile eye
tracker [4]. Our research follows the path yet we focus on

more detail part in manga analysis. We recognize readers’ eyegaze information on different types of content in a manga page(e.g. on text, comic character, and background) and use thisinformation for the analysis of manga layout.
B. Manga image analysis
Manga image analysis is a quite recent and challenging fieldof research that attracts more and more attention according to
the growing demand of the mobile manga reading market. Text
and graphics analysis was studied for decades on different document images including newspaper, administrative documents,
checks and graphical documents such as maps and engineeringdrawings [10]. More recently, manga image analysis has been
investigated with the generalization of mobile devices forreading activities. Manga images have a very specific layoutthat guides the reader through the story. The early approaches
developed toward layout analysis concerned the reading orderof the panels [11], [12].
Text analysis in manga images opens up several interesting
applications such as image compression [13] and content retargeting to different mobile reading devices [14]. Despite thegrowing interest, text extraction from manga image remainsa challenging problem because mangas are a mixture of text,
graphics and graphic sounds with strong relationships [15].
The efficiency of combining text and speech balloon features
to extract both of them has been highlighted recently [16]. The
benefits of using eye-tracker for text extraction has alreadybeen demonstrated as in [2], [17]. Few work about comic
character extraction have been published until now. Recently,
we improved Sun’s method to fit character retrieval for
manga [18]. We demonstrated that few manually labeled data
can make the detection more robust against various posturesand facial expression [19]. We recently demonstrate thatspeech balloon and tail detection allow manga character regionof interest extraction and character-to-speech association [20],[21].
III. P ROPOSED APPROACH
The proposed approach consists in eye gaze data acquisition,filtering and analysis. These three steps are detailed in thefollowing subsections.
A. Eye gaze data processing
Human’s eye movements are classified into fixations andsaccades. A fixation is an attention on a single location of thevision, a saccade is a quick jump between two fixations [7]. We
detect fixations and saccades from raw eye gaze data as shownin Fig. 2 and 3. The radius of each circle correspond to thefixation duration and the line between two circles represents
a saccade. Noises of the eye tracking device are also filteredin this process. The fixation-saccade detecting algorithm isbased on Buscher’s approach [22]. Fig. 4 shows the processof the algorithm. First, a new minimum fixation is detected if
4 consecutive points are in a rectangle of T h1 pixels (e.g. p1 ,p2 , p3 and p4 in Fig. 4). Four consecutive points from 42Hz
represents human’s minimum fixation duration (80-100ms). If

Fig. 2. Non filtered raw eye gaze data from eye tracking device.

(a) Fixation heat map (single user)
Fig. 3. Filtered output representing fixations (circles) and saccades (lines).

(b) Corresponding regions

Fig. 5. Fixation heat map and corresponding region. Image credits: [6].

B. Fixation and saccade analysis

Fig. 4. Fixation and saccade detection process from eye gaze points. Two
fixations and one saccade are computed from twelve non-filtered gaze points.

the length of a side of the rectangle including the next gazepoint is smaller than T h2 pixels, we include it and make therectangle bigger. Yet if the position of gaze point is outsideof rectangle with T h2 pixels, we remove the point as a noise(e.g. p5 and p8 in Fig. 4). If 3 consecutive points are out of therectangle (e.g. p8 , p9 and p10 in Fig. 4), we start to detect thenext minimum fixation from the first point which is outside
the rectangle (e.g. p8 in Fig. 4). Finally, we combine gazepoints in each rectangle and store the center of mass as thecoordinate of the fixation and duration between the time stampof first to last point in the rectangle.
In order to enhance visualization of multiple reader eye
gaze behaviors, we calculate the average of data of multiple
readers and generate two kinds of heat map. The first heat map
corresponds to the distribution of saccades. We draw saccade
path as a line whose width is 30 pixel and overlay multiple
readers’ paths on one image. An example with 25 readers ispresented in Fig. 1.

Filtered eye gaze data contain eye fixation positions andduration. We would like to use them for text ad “important”
graphic region extractions (e.g. manga characters).
1) Text region extraction: For text regions, our brain requires much more effort to recognize, understand, and process
the information, compare to understanding graphic regionsin general (text in not natural). Therefore, fixation duration
analysis is relevant for text region extraction, especially when
the fixations are spatially close and the duration is significantly
longer. We propose to combine position and duration of
fixation points in order to extract text regions from the images.
The combination is done by using a Gaussian distribution
centered on each fixation. The distribution parameters are thefixation coordinate (µ) and a factor of duration as standard
deviation (σ). The duration factor σ is computed from a
distance in pixel N multiplied by the fixation duration. The
distance N has been set to 4% of the image size which roughly
correspond to the size of a text line into a speech balloon ofmanga (chosen by experiments). All the distributions are thensummed and the important overlap between spatially close
distribution makes them easily distinguishable on a heat map
view for instance (Fig. 5a). Then we extract text regions using
a connected component analysis on a binary segmentation of
the distributions (Fig. 5b). The threshold used for the binary
segmentation was fixed to 50% of the total distribution value
range in order to extract the user’s main fixation regions only.
Note that the extracted text regions roughly correspond to
speech balloon regions.
2) Graphics extraction: Graphics region can be decomposed into two categories: the foreground containing most of
the focus attention of the story, and the background which
is more for illustration purpose. In this paper we focus onforeground content extraction which is relevant in the context
of manga understanding. As introduced in Section II-B, offline
image analysis still quite weak at extracting graphics in(a) Original image(b) Panel extraction(c) Background extraction

Fig. 7. Image background extraction process. Image credits: [6].

(a) Saccade segments (blue lines)(b) Corresponding regions

Fig. 6. Saccade heat map and corresponding region. Image credits: [6].

manga because their appearance change a lot within the pages,
titles and even more between different titles from different
artists (e.g. deformation, occlusion, perspective, scale change).
Online analysis has the advantage of providing the watchedregions encoded as saccades map (Fig. 6a).
Saccades are deduced from jumps between to fixations
as introduced in Section III-A. They do not correspond to
well delimited graphics in the image but overlap part of theforeground regions in each panel. Sometimes, there is also
short fixations on the graphics (Fig. 3). The human brain
don’t necessarily need a fixation to understand the “content”.
He can “catch” (or understand) the meaning of the picture
also instantaneously. A saccade has the advantage to passthrough important graphic regions chosen by the reader tounderstand the story but at the end it is only a 1D segment
which is not easily convertible as a 2D region corresponding
to a graphic region in the image. Note that we manuallyenlarged the saccade segment in order to obtain a 2D region
representative of the main foreground elements that have beenvisualized by the reader (Fig. 6b). The enlargement is evenmore accurate when computed from several user’s saccade
paths (Section IV-B2). Nevertheless, saccade segments are notpassing only through important foreground regions. They arealso sometimes overlapping text and page background regions.
We propose to remove these two information by combining thepreviously computed text regions (online) and the inverse ofpanel extraction results (offline).
Panel extraction algorithms aim to segment panel fromimage background (usually white). In our case, we are interested by extraction the background region which is simply
the inverted output of panel extraction (Fig. 7). As introducedin Section II-B, several panel extractor algorithms have been
proposed, we use our latest contribution but any of them could
be used [20]. This panel extractor is based on dark connectedcomponent topology analysis (panels are not included in other(a) Originalpathsaccade (b) Balloon and back- (c) Final region mask
ground subtraction

Fig. 8. Graphics region extraction process. Image credits: [6].

elements).
Finally, from the original saccade path we remove text
and background regions to isolate the most watched graphic
regions (Fig.8).
IV. E XPERIMENTS
In this section we introduce the experimental setup and thenwe evaluate the proposed text and graphic detection methods.
A. Experimental setup
The eye gaze data are recorded while the manga pages arebeing viewed on the screen by the participants, the setup is
shown as in Fig. 9. We use Tobii eyeX 1 , an inexpensive eye
tracking device from Tobii Technology, at sampling rate of
42Hz. The device only requires a short calibration for eachuser, and allows them to move their head freely in frontof the screen, although users are requested to try to keepa fixed distance to the screen to attain the best accuracy.
The tracker operates based on corneal reflection technique:multiple infrared diodes, invisible to the human eyes, are used
to create a reflection on the cornea, these reflections are thencaptured by the tracker’s sensors and used to model the eyesin three dimensions and to calculate the gaze point. We asked
25 participants of both genders, from different ages (14 to
45 year old) and nationalities (French, Tunisian, Pakistani,
1 http://www.tobii.com/eyex/a0 =area(Bp ∩ Bgt )area(Bp ∪ Bgt )(1)

Detected regions were assigned to ground truth objects
according to maximum overlap criterion. Multiple detection of
the same object in an image were considered false detection.
The number of T P , F P and false negative (missed elements)
F N was used to compute the recall R and the precision P ofeach of the methods using Formula 2 and 3. We also computed
the F-measure F for each result.
R=
Fig. 9. Data recording setting with Tobii eyeX fixed under the top screen.

TP
TP + FN

(2)

TP
(3)
TP + FP
1) Text regions: We evaluated the proposed text regions
extractor to a state of the art offline method proposed by ourteam [16] which is able to extract speech balloon if they contain text-like elements (e.g. similar height, aligned, centered).
Note that we did not compare to a text line extraction method
because fixation and saccades analysis provides regions thatare more similar to speech balloon than text lines. Speechballoon extraction performance are presented Table I.
P =Cambodian, Colombian, Indonesian, Vietnamese, Japanese).
All the participants were asked to read the same chapter froma famous Japanese manga [6]. We choose to let users stayon each image as long as they need because each participant
has different speed of reading/understanding, or behavior, or
what we call “manga literacy”. The readers are guided toadvance through the images by pressing on the keyboard.
This may sometimes leads to outlier points where the eyes
move out of the screen to seek the key, but those points
can be filtered out quite easily. The image set is a wholechapter in the title, containing 20 pages in total, presented as
double pages which resulted in 10 images in total. It contains
139 speech balloons and 124 main manga character instances.
The resolution of each image is 1683x1200 pixels. Since theparticipants have different native languages, two versions wereavailable for choosing (English and French) in order to provide
the best natural reading experience. Fig.9 shows the overview
of experiment setup. According to the image size, we set
thresholds for fixation-saccade detection T h1 and T h2 as 50
and 80 pixels respectively. Note that the way of detecting
the eyes, calculating the gaze position, the calibrating and theenvironment can all affect the accuracy of the eye tracker.
B. Evaluation
We compared the proposed online text and graphic detection
methods to offline methods used for speech balloon and manga
character extraction.Note that we performed the experiment,
for each image, on the average results of all the participantsin order to reduce the impact of participant variability suchas gender, culture, age, unusual reading direction etc. (Fig. 1).
The comparison between extraction regions and ground truth
region were performed at object level such as the PASCAL
VOC challenge [23]. In this challenge, the detection wereassigned to ground truth objects and judged to be true or
false positives by measuring bounding box overlap. To beconsidered a correct detection, the overlap ratio a0 between thepredicted bounding box Bp and the ground truth bounding box
Bgt must exceed 0.5 (Formula 1). The predicted objects wereconsidered true positives T P if a0 > 0.5 or false positives
F P (prediction errors).

TABLE IS PEECH BALLOON SEGMENTATION PERFORMANCE IN PERCENT.

MethodRigaud [16]Proposed

R
74.10
68.84

P
60.95
95.95

F1
66.88
80.17

The best recall concerns the offline method because thepresented online method misses speech balloons that containfew text and thus require a very short fixation to be extracted
compared to usual balloons. However, the proposed method
has an excellent precision because it is based on user’s eye
movement which is highly reliable.
2) Graphics regions: We compared the proposed graphics
extraction method to a state of the art method able to extract
manga character faces [19]. We chose this method because it is
the most similar to our proposal which also focus on character
faces (highly viewed by the reader). Results are presented
Table II for a0 = 0.2 in order to accept more partial faces
as correct.
TABLE IIC HARACTER FACE SEGMENTATION PERFORMANCE IN PERCENT.

MethodIwata [19]Proposed

R
54.90
95.96

P
72.72
66.11

F1
62.57
78.28

The comparison between the offline method and the proposed method are quite interesting. Even through we relaxed
the overlap criterion which means that the faces may only
be detected at 20% of their total surface, the recall is very

good. This verifies that the eye movements are really reliable
to localize main manga faces or character parts because itis passing over most of them. However, the precision of the
proposed method is lower than the offline method because
saccade paths are continuous and thus overlap other regions
as well.
V. D ISCUSSION
The presented method relies on fixation and duration computed from eye gaze movements. Although a calibration step
was performed for each reader before the recording, an offset
may appear if the reader changes position while reading. We
were not able to measure this offset which may shift fixation
positions but not duration. This first work has been performed
on a small set of manga images to validate the use of eye
gaze information for online image analysis with Latin script.
It could easily be extended to Franco-Belgium and American
comics. The proposed method requires users to read the image
which could be a constraint of the system. However, this could
be done withing the editorial process where the author could
invite some people to first read his work.
VI. C ONCLUSION
In this paper we presented a semi-automatic extraction
methods for text and graphics regions. The proposed method isbased on eye gaze fixation and saccade analysis and works bestwhen several readers are involved into the data acquisition.
This is a preliminary work but it opens up several new waysof research. In the future will would like to link text tographic regions in order to retrieve the associations between
speech text and speaking characters. More generally, topdown knowledge-driven analysis could benefit from eye gazedata to interact more precisely with bottom-up information.
Another use of this online method could be crowd-based
groundtruthing.
ACKNOWLEDGMENT
This work was mainly supported by the University of La
Rochelle (France) and the bilateral program Sakura between
Campus France (PHC) and the Japan Society for the Promotion of Science (JSPS). It was also supported in partby JST CREST, JSPS Kakenhi, Grant Number 25240028
and 15K12172 from Japan. We are grateful to all authors
and publishers of comics and manga from eBDtheque andKingdom datasets for allowing us to use their works.
R EFERENCES[1] A. O. Mohamed, M. P. Da Silva, and V. Courboulay, “A history of eye
gaze tracking,” 2007.
[2] K. Kunze, H. Kawaichi, K. Yoshimura, and K. Kise, “The wordometer–
estimating the number of words read using document image retrieval andmobile eye tracking,” in Document Analysis and Recognition (ICDAR),
2013 12th International Conference on. IEEE, 2013, pp. 25–29.
[3] A. Okoso, T. Toyama, K. Kunze, J. Folz, M. Liwicki, and K. Kise,“Towards extraction of subjective reading incomprehension: Analysis ofeye gaze features,” in Proceedings of the 33rd Annual ACM Conference
Extended Abstracts on Human Factors in Computing Systems. ACM,
2015, pp. 1325–1330.

[4] K. Kunze, Y. Utsumi, Y. Shiga, K. Kise, and A. Bulling, “I knowwhat you are reading: recognition of document types using mobile eye
tracking,” in Proceedings of 17th annual international symposium on
International symposium on wearable computers, 2013, pp. 113–116.
[5] W. Eisner, Comics and Sequential Art: Principles and Practices from the
Legendary Cartoonist (Will Eisner Instructional Books). WW Norton& Company, 2008.
[6] Y. Hara, Kingdom chapter 175 - Riboku, Kanyou Bound. Chiyoda,
Tokyo: Shueisha Inc., 2009, vol. 17.
[7] A. Bulling, J. A. Ward, H. Gellersen, and G. Tröster, “Eye movement
analysis for activity recognition,” in Proceedings of 11th International
Conference on Ubiquitous Computing, 2009, pp. 41–50.
[8] S. Ishimaru, K. Kunze, K. Tanaka, Y. Uema, K. Kise, and M. Inami,“Smart eyewear for interaction and activity recognition,” in Proceedingsof the 33rd Annual ACM Conference Extended Abstracts on Human
Factors in Computing Systems. ACM, 2015, pp. 307–310.
[9] R. Biedert, J. Hees, A. Dengel, and G. Buscher, “A robust realtime
reading-skimming classifier,” in Proceedings of the Symposium on Eye
Tracking Research and Applications. ACM, 2012, pp. 123–130.
[10] G. Nagy, “Twenty years of document image analysis in pami,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 22,
no. 1, pp. 38–62, 2000.
[11] C. Guérin, “Ontologies and spatial relations applied to comic books
reading.” in PhD Symposium of Knowledge Engineering and Knowledge
Management (EKAW), Galway, Ireland, 2012.
[12] L. Li, Y. Wang, Z. Tang, and L. Gao, “Automatic comic page segmentation based on polygon detection,” Multimedia Tools Applications,vol. 69, no. 1, pp. 171–197, 2014.
[13] C.-Y. Su, R.-I. Chang, and J.-C. Liu, “Recognizing text elements
for svg comic compression and its novel applications,” inProceedings of International Conference on Document Analysis
and Recognition (ICDAR).
Washington, DC, USA: IEEE
Computer Society, 2011, pp. 1329–1333. [Online]. Available:
http://dx.doi.org/10.1109/ICDAR.2011.267[14] Y. Matsui, T. Yamasaki, and K. Aizawa, “Interactive Manga retargeting,”
in ACM SIGGRAPH 2011 Posters on - SIGGRAPH ’11. New York,New York, USA: ACM Press, 2011, p. 1. [Online]. Available:
http://dl.acm.org/citation.cfm?doid=2037715.2037756[15] A. Ghorbel, J.-M. Ogier, and N. Vincent, “Text extraction from comic
books,” in Proceedings of the 11th IAPR International Workshop on
Graphics Recognition (GREC), Nancy, France, 2015.
[16] C. Rigaud, J.-C. Burie, and J.-M. Ogier, “Text-independent speechballoon segmentation for comics and manga,” in Proceedings of the
11th IAPR International Workshop on Graphics Recognition (GREC),
Nancy, France, 2015.
[17] A. Clavelli, D. Karatzas, J. Llads, M. Ferraro, and G. Boccignone,“Modelling task-dependent eye guidance to objects in pictures,”
Cognitive Computation, vol. 6, no. 3, pp. 558–584, 2014. [Online].
Available: http://dx.doi.org/10.1007/s12559-014-9262-3[18] W. Sun and K. Kise, “Similar partial copy recognition for line drawings
using concentric multi-region histograms of oriented gradients,” inProceedings of the IAPR Conference on Machine Vision Applications,ser. MVA2011, Nara, JAPAN, June 13-15, 2011.
[19] M. Iwata, A. Ito, and K. Kise, “A study to achieve manga character
retrieval method for manga images,” in Proceedings of the 11th IAPR
International Workshop on Document Analysis Systems (DAS2014), Apr.
2014, pp. 309–313.
[20] C. Rigaud, C. Gurin, D. Karatzas, J.-C. Burie, and J.-M. Ogier,“Knowledge-driven understanding of images in comic books,”International Journal on Document Analysis and Recognition(IJDAR), vol. 18, no. 3, pp. 199–221, 2015. [Online]. Available:
http://dx.doi.org/10.1007/s10032-015-0243-1[21] C. Rigaud, N. Le Thanh, J.-C. Burie, J.-M. Ogier, M. Iwata, E. Imazu,
and K. Koichi, “Speech balloon and speaker association for comics
and manga understanding,” in Proceedings of the 13th International
Conference on Document Analysis and Recognition (ICDAR). To be
published. IEEE, 2015.
[22] G. Buscher, A. Dengel, and L. van Elst, “Eye movements as implicit
relevance feedback,” in CHI’08 extended abstracts on Human factors incomputing systems. ACM, 2008, pp. 2991–2996.
[23] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International
journal of computer vision, vol. 88, no. 2, pp. 303–338, 2010.