A Study to Achieve Manga Character Retrieval Method for Manga Images

Iwata, Mamoru; Ito, Atsushi; Kise, Koichi

doi:10.1109/das.2014.60

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…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 postures and facial expression [19]. We recently demonstrate that speech balloon and tail detection allow manga character region of interest extraction and character-to-speech association [20], [21].…”

Section: B Manga Image Analysismentioning

confidence: 97%

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Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Rigaud¹,

Le²,

Burie³

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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International audienceThe 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 the graphic 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 based on 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, and can be used for manga textual and graphical analysis

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Section: B Manga Image Analysismentioning

confidence: 97%

“…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).…”

Section: ) Text Regionsmentioning

confidence: 99%

Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Rigaud¹,

Le²,

Burie³

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, we improved Sun's method to fit character retrieval for manga. It is more robust against various postures and facial expressions using a few labeled data [18]. We also proposed a generic and unsupervised character extraction approach from contextual information which estimates character regions of interest from the positions of balloons inside panels [15].…”

Section: Comic Character Extractionmentioning

confidence: 99%

Speech balloon and speaker association for comics and manga understanding

Rigaud

Thanh

Burie

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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Comics and manga are one of the most important forms of publication and play a major role in spreading culture all over the world. In this paper we focus on balloons and their association to comic characters or more generally text and graphic links retrieval. This information is not directly encoded in the image, whether scanned or digital-born, it has to be understood according to other information present in the image. Such high level information allows new browsing experience and story understanding (e.g. dialog analysis, situation retrieval). We propose a speech balloon and comic character association method able to retrieve which character is emitting which speech balloon. The proposed method is based on geometric graph analysis and anchor point selection. This work has been evaluated over various comic book styles from the eBDtheque dataset and also a volume of the Kingdom manga series.

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“…The work in [53] shows good results for character retrieval using local feature extraction and the approximate nearest neighbors (ANN) search. In [54], authors used Frequent Subgraph Mining (FSM) techniques for comic image browsing using query-by-example (QBE) model.…”

Section: Comic Charactersmentioning

confidence: 99%

“…Some other works focus on character retrieval [53][54][55]. The work in [53] shows good results for character retrieval using local feature extraction and the approximate nearest neighbors (ANN) search.…”

Section: Comic Charactersmentioning

confidence: 99%

Digital Comics Image Indexing Based on Deep Learning

2018

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Abstract:The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.

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A Study to Achieve Manga Character Retrieval Method for Manga Images

Cited by 8 publications

References 6 publications

Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Speech balloon and speaker association for comics and manga understanding

Digital Comics Image Indexing Based on Deep Learning

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