The Graphic Narrative Corpus (GNC): Design, Annotation, and Analysis for the Digital Humanities

Dunst, Alexander; Hartel, Rita; Laubrock, Jochen

doi:10.1109/icdar.2017.286

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…The Graphic Narrative Corpus (GNC; Dunst et al, ) has been curated with the specific goal to create a data set useful for research in the humanities, aiming for representativeness and attempting to balance popularity and prestige. The GNC focuses on English‐language graphic novels and currently contains about 270 titles with approximately 55,000 pages.…”

Section: Data Setsmentioning

confidence: 99%

Computational Approaches to Comics Analysis

Laubrock

Dunst

2019

Topics in Cognitive Science

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Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling the stimulus ideally requires a formal description, which can be provided by feature descriptors from computer vision and computational linguistics. With a focus on document analysis, here we review work on the computational modeling of comics. We argue that the development of modern feature descriptors based on deep learning techniques has made sufficient progress to allow the investigation of complex material such as comics for reception studies, including experimentation and computational modeling of cognitive processes.

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Section: Data Setsmentioning

confidence: 99%

Computational Approaches to Comics Analysis

Laubrock

Dunst

2019

Topics in Cognitive Science

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“…The Graphic Narrative Corpus (GNC) [20] provide metadata information for 207 titles such as the authors, number of pages, illustrators, genres, etc. Unfortunately, the corresponding images are not available because of copyright protections.…”

Section: Datasetsmentioning

confidence: 99%

A Survey of Comics Research in Computer Science

2018

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Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, humancomputer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks.

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“…Fully automated analyses of such graphic novels are not yet feasible (beyond recognizing text there are even more difficult challenges, such as the recognition of narrative characters or the pointof-view of a panel). Therefore, our project semi-automatically annotates a corpus of currently around 220 graphic novels, memoirs, and non-fiction, which we call the Graphic Narrative Corpus (GNC) with the help of the M3-Editor developed as part of our project [2].…”

Section: Our Projectmentioning

confidence: 99%

“…For our evaluation, we used the graphic narrative corpus (GNC) [2] ], which was designed as a representative corpus as part of our project. As we need a ground truth in order to evaluate the results and the annotation of a graphic novel is very time-consuming (especially the transcription of the texts), only the part of our corpus that already has been completely annotated could be used.…”

Section: Corpusmentioning

confidence: 99%

"How good is good enough?" Establishing quality thresholds for the automatic text analysis of retro-digitized comics

Hartel¹,

Dunst²

2018

EasyChair Preprints

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Stylometry in the form of simple statistical text analysis has proven to be a powerful tool for text classification, e.g. in the form of authorship attribution. When analyzing retro-digitized comics, manga and graphic novels, the researcher is confronted with the problem that automated text recognition (ATR) still leads to results that have comparatively high error rates, while the manual transcription of texts remains highly time-consuming. In this paper, we present an approach and measures that specify whether stylometry based on unsupervised ATR will produce reliable results for a given da-taset of comics images.

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The Graphic Narrative Corpus (GNC): Design, Annotation, and Analysis for the Digital Humanities

Cited by 22 publications

References 6 publications

Computational Approaches to Comics Analysis

Computational Approaches to Comics Analysis

A Survey of Comics Research in Computer Science

"How good is good enough?" Establishing quality thresholds for the automatic text analysis of retro-digitized comics

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