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We will profit from deep learning recognition models to also recognize text. 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). In parallel, deep learning approaches are becoming more and more popular by overpassing traditional methods in numerous domains using machine learning techniques. These challenges make the task of content learning and recognition much harder. The challenges that are facing researchers dealing with comic book images are related to the diversity of styles, layout, text, and actions from a mixture of representations of real and imaginary worlds. The more elements we are able to extract and recognize automatically, the better the story reconstruction quality and the result image relevances will be. Comic book contents (e.g., panels, balloons, texts, comic characters, etc.) and their relations (e.g., read before, said by, thought by, and addressed to) have to be automatically recognized to reconstruct the story and to keep the story coherent. While information retrieval research domain has a long history, content-based comics indexing and the creation of comics in a digital form are quite recent. This field of research includes content segmentation, relation analysis, style analysis and information retrieval. Digital comic book analysis can then be investigated to help the creation or conversion of comics in an enhanced digital form and to improve userвЂ™s reading and browsing experiences.

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A panel being a subpart of a comic page, it may be more relevant to return to the user a panel region than the entire page, especially if the query is written or drawn inside a single panel (returning the full image would result in a lot of unwanted information for the user).

Regarding comics, there are extra difficulties related to the image layout for specifying a query and finding results at page and/or panel level. We propose to adapt such techniques to specific images as comic strip images. Breakthrough techniques based on deep learning started to improve image retrieval relevancy only a few years ago.

To improve web image retrieval performance, we have to fuse the evidences from textual and visual contents.

This is a time-consuming process as the returned results almost always contain multiple topics which are mixed together. The result is that users often have to go through a long list to eventually find the desired images. Although these approaches can find many relevant images, the retrieval precision is poor as they cannot confirm whether the retrieved images indeed contain the query elements.

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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 ().Д®xisting web image search engines such as Google, Yahoo, Bing and Qwant search images based on textual evidences from text around the images in web pages. There are also online libraries selling digital and physical comic books such as DC Comics from Warner Bros (), comiXology (), Sequencity (), Izneo () and Koomic ().

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Physical museums such as the 'Internationale de la Bande DessinГ©e et de lвЂ™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.