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Multi-task Model for Comic Book Image Analysis

  • Nhu-Van NguyenEmail author
  • Christophe Rigaud
  • Jean-Christophe Burie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

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.

Keywords

Comic book image analysis Association balloon-character Multi-task learning CNN Deep learning 

Notes

Acknowledgement

This work is supported by the CPER NUMERIC programme funded by the Region Nouvelle Aquitaine, CDA, Charente Maritime French Department, La Rochelle conurbation authority (CDA) and the European Union through the FEDER funding”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nhu-Van Nguyen
    • 1
    Email author
  • Christophe Rigaud
    • 1
  • Jean-Christophe Burie
    • 1
  1. 1.Laboratoire L3iUniversité de La RochelleLa Rochelle CEDEX 1France

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