Multi-task Model for Comic Book Image Analysis

Comic book image analysis methods often propose multiple algorithms or models for multiple tasks like panels and characters detection, balloons segmentation and text recognition, etc. In this work, we aim to reduce the complexity for comic book image analysis by proposing one model which can learn multiple tasks called Comic MTL. In addition to the detection task and segmentation task, we integrate the relation analysis task for balloons and characters into the Comic MTL model. The experiments with our model are carried out on the eBDtheque dataset which contains the annotations for panels, balloons, characters and also the relations balloon-character. We show that the Comic MTL model can detect the association between balloons and their speakers (comic characters) and handle other tasks like panels, characters detection and balloons segmentation with promising results.