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J. Imaging 2018, 4(7), 89; https://doi.org/10.3390/jimaging4070089

Digital Comics Image Indexing Based on Deep Learning

Lab L3I, University of La Rochelle, 17000 La Rochelle, France
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Received: 30 April 2018 / Revised: 21 June 2018 / Accepted: 27 June 2018 / Published: 2 July 2018
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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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. View Full-Text
Keywords: comics analysis; image indexing; deep learning; CNN; LSTM; handwritten text recognition comics analysis; image indexing; deep learning; CNN; LSTM; handwritten text recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Nguyen, N.-V.; Rigaud, C.; Burie, J.-C. Digital Comics Image Indexing Based on Deep Learning. J. Imaging 2018, 4, 89.

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