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Comic MTL: optimized multi-task learning for comic book image analysis

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Abstract

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.

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Notes

  1. http://digitalcomicmuseum.com.

  2. https://git.univ-lr.fr/crigau02/dcm_dataset/tree/master.

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Acknowledgements

This work is supported by the Research National Agency (ANR) in the framework of the 2017 LabCom program (ANR 17-LCV2-0006-01), the CPER NUMERIC program 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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Nguyen, NV., Rigaud, C. & Burie, JC. Comic MTL: optimized multi-task learning for comic book image analysis. IJDAR 22, 265–284 (2019). https://doi.org/10.1007/s10032-019-00330-3

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