How good is good enough? Establishing quality thresholds for the automatic text analysis of retro-digitized comics
Hartel R, Dunst A (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11296 LNCS
Pages Range: 662-671
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Thessaloniki, GRC
ISBN: 9783030057152
DOI: 10.1007/978-3-030-05716-9_59
Abstract
Stylometry in the form of simple statistical text analysis has proven to be a powerful tool for text classification, e.g. in the form of authorship attribution. When analyzing retro-digitized comics, manga and graphic novels, the researcher is confronted with the problem that automated text recognition (ATR) still leads to results that have comparatively high error rates, while the manual transcription of texts remains highly time-consuming. In this paper, we present an approach and measures that specify whether stylometry based on unsupervised ATR will produce reliable results for a given dataset of comics images.
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Germany (DE)How to cite
APA:
Hartel, R., & Dunst, A. (2019). How good is good enough? Establishing quality thresholds for the automatic text analysis of retro-digitized comics. In Benoit Huet, Ioannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Cathal Gurrin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 662-671). Thessaloniki, GRC: Springer Verlag.
MLA:
Hartel, Rita, and Alexander Dunst. "How good is good enough? Establishing quality thresholds for the automatic text analysis of retro-digitized comics." Proceedings of the 25th International Conference on MultiMedia Modeling, MMM 2019, Thessaloniki, GRC Ed. Benoit Huet, Ioannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Cathal Gurrin, Springer Verlag, 2019. 662-671.
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