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The last element we would like to deal with is the text which relates to the detection and recognition tasks. This task can be performed using deep learning segmentation models. While a bounding box may be sufficient to represent a panel/character position, balloons require a more precise boundaries description according to their more rounded shape. The first one is to detect objects such as panels and comic characters by determining the bounding box of the objects. We have identified three kinds of task for extracting comic elements. We present how deep learning can be beneficial for comic book image analysis, by applying several Convolutional Neural Network (CNN) models to extract each element contained by these types of images (e.g., panel, balloon, text, and comic character). In this work, we would like to highlight the advantages of combining traditional and deep learning based approaches and show how they can benefit from each other when applied to comic book image understanding (see Figure 1).
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In parallel, deep learning approaches are becoming more and more popular by overpassing traditional methods in numerous domains using machine learning techniques.
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Other websites host a huge amount of comics and manga created by amateurs for free reading purpose, based on a Paid To Click business model, e.g., MangaFox ( ) and MangaHere ( ). There are also online libraries selling digital and physical comic books such as DC Comics from Warner Bros ( ), comiXology ( ), Sequencity ( ), Izneo ( ) and Koomic ( ). Physical museums such as the "Internationale de la Bande Dessinée et de l’image" ( ) in France and the Kyoto International Manga Museum ( ), online museums or archives such as The Digital Comic Museum ( ), Comic Book Plus ( ) and the Grand Comics Database ( ) have already digitized several thousands of comic albums and some are in the public domain. Initially, comics were printed on paper books, but nowadays, born-digital comic books have become more and more popular.