Abstract
Comic books digitization combined with subsequent comic book understanding give rise to a variety of new applications, including content reflowing, mobile reading and multi-modal search. Document understanding in this domain is challenging as comics are semi-structured documents, with semantic information shared between the graphical and textual parts. Speech balloon contour analysis reveals the speech tone which is an essential step towards a fully automatic comics understanding. In this paper we present the first approach for classifying speech balloon in scanned comic books where we separate and analyze their contour variations to classify them as “smooth” (normal speech), “wavy” (thought) or “zigzag” (exclamation). The experiments show a global accuracy classification of 85.2 % on a wide variety of balloons from the eBDtheque dataset.
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Acknowledgment
This work was supported by the European Doctorate founds of the University of La Rochelle, the European Regional Development Funds, the region Poitou-Charentes (France), the General Council of Charente Maritime (France), the town of La Rochelle (France) and the Spanish research projects TIN2011-24631, RYC-2009-05031. The authors would like to thanks Audrey Adam for her tedious work on the construction of the pixel level ground truth.
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Rigaud, C., Karatzas, D., Burie, JC., Ogier, JM. (2014). Adaptive Contour Classification of Comics Speech Balloons. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_5
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DOI: https://doi.org/10.1007/978-3-662-44854-0_5
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