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Comic book image analysis methods often propose multiple algorithms or models for multiple tasks like panel and character (body and face) detection, balloon segmentation, text recognition, etc. In this work, we aim to reduce the processing time for comic book image analysis by proposing one model that can learn multiple tasks called Comic MTL instead of using one model per task. In addition to detection and segmentation tasks, we integrate the relation analysis task for balloons and characters into the Comic MTL model. The experiments are carried out on DCM772 and eBDtheque public datasets that contain the annotations for panels, balloons, characters and also the associations between balloon and character. We show that the Comic MTL model can detect the associations between balloons and their speakers (comic characters) and handle other tasks like panel and character detection and also balloons segmentation with promising results. [ABSTRACT FROM AUTHOR]

Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book's and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. [ABSTRACT FROM AUTHOR]