Computational Approaches to Comics Analysis

Laubrock, Jochen; Dunst, Alexander

doi:10.1111/tops.12476

Cited by 14 publications

(5 citation statements)

References 118 publications

(157 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While these efforts show the growing effectiveness of machine learning for recognizing targeted aspects of visual narratives, such methods often underperform both human testers and computational methods trained on naturalistic photographs (Khetarpal & Jain, ; Takayama, Johan, & Nishita, ). However, significant advances are being made with such computational methods, which are rapidly becoming more reliable (see Laubrock & Dunst, ).…”

Section: Aspects Of Visual Narrativesmentioning

confidence: 99%

See 1 more Smart Citation

Editors’ Introduction and Review: Visual Narrative Research: An Emerging Field in Cognitive Science

Cohn

Magliano

2019

Topics in Cognitive Science

View full text Add to dashboard Cite

Drawn sequences of images are among our oldest records of human intelligence, appearing on cave paintings, wall carvings, and ancient pottery, and they pervade across cultures from instruction manuals to comics. They also appear prevalently as stimuli across Cognitive Science, for studies of temporal cognition, event structure, social cognition, discourse, and basic intelligence. Yet, despite this fundamental place in human expression and research on cognition, the study of visual narratives themselves has only recently gained traction in Cognitive Science. This work has suggested that visual narrative comprehension requires cultural exposure across a developmental trajectory and engages with domain-general processing mechanisms shared by visual perception, attention, event cognition, and language, among others. Here, we review the relevance of such research for the broader Cognitive Science community, and make the case for why researchers should join the scholarship of this ubiquitous but understudied aspect of human expression.

show abstract

Section: Aspects Of Visual Narrativesmentioning

confidence: 99%

“…Recent efforts using computational methods have also begun analyzing visual narrative sequencing (see Laubrock & Dunst, ). Some marginal results have been found by convolutional neural networks in recognizing sequencing structure in four‐panel comic strips (Ueno & Isahara, ; Ueno, Mori, Suenaga, & Isahara, ).…”

Section: Aspects Of Visual Narrativesmentioning

confidence: 99%

Editors’ Introduction and Review: Visual Narrative Research: An Emerging Field in Cognitive Science

Cohn

Magliano

2019

Topics in Cognitive Science

View full text Add to dashboard Cite

show abstract

“…• Line-level, word-level, and character-level segmentation: Segmentation divides the entire image into subimages to process them further. The most popular techniques used for image segmentation are: X-Y-tree decomposition [96], connected component labeling [111], Hough transforms [96], and histogram projection techniques [66], [34].…”

Section: • •mentioning

confidence: 99%

“…The YOLO algorithm performs far better than any other algorithm, as its training and classification time is very less for real-time object detection and reaction. CNN YOLO is also used for object recognition and feature extraction in complex documents such as comics [111]. • BERT: Few studies [83], [13] reported the use of BERT for extracting features from the data.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

View full text Add to dashboard Cite

The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

show abstract

“…At present, a number of research groups are working to push the limits of what computer vision can do with comics (cf. Laubrock and Dubray 2019;Laubrock and Dunst 2019;Young-Min 2019). Any such research is, of course, heavily dependent on the availability of appropriate training sets.…”

Section: New Digital Approaches To Visual Analysis and Art Historymentioning

confidence: 99%

Персонажи «Войны И Мира» Л. Н. Толстого: Вхождения В Тексте, Прямая Речь И Семантические Роли

Скоринкин¹

Репозиторий Открытых Данных По Русской Литературе И Фольклору

View full text Add to dashboard Cite

sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Computational Approaches to Comics Analysis

Cited by 14 publications

References 118 publications

Editors’ Introduction and Review: Visual Narrative Research: An Emerging Field in Cognitive Science

Editors’ Introduction and Review: Visual Narrative Research: An Emerging Field in Cognitive Science

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Персонажи «Войны И Мира» Л. Н. Толстого: Вхождения В Тексте, Прямая Речь И Семантические Роли

Contact Info

Product

Resources

About