Comic text detection using neural network approach

Frédéric Rayar, Seiichi Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Text is a crucial element in comic books; hence text detection is a significant challenge in an endeavour to achieve comic processing. In this work, we study in what extent an off-the-shelf neural network approach for scene text detection can be used to perform comic text detection. Experiment on a public data set shows that such an approach allows to perform as well as methods of the literature, which is promising for building more accurate comic text detector in the future.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings
EditorsBenoit Huet, Ioannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Cathal Gurrin
PublisherSpringer Verlag
Pages672-683
Number of pages12
ISBN (Print)9783030057152
DOIs
Publication statusPublished - Jan 1 2019
Event25th International Conference on MultiMedia Modeling, MMM 2019 - Thessaloniki, Greece
Duration: Jan 8 2019Jan 11 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11296 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on MultiMedia Modeling, MMM 2019
CountryGreece
CityThessaloniki
Period1/8/191/11/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Rayar, F., & Uchida, S. (2019). Comic text detection using neural network approach. In B. Huet, I. Kompatsiaris, S. Vrochidis, V. Mezaris, W-H. Cheng, & C. Gurrin (Eds.), MultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings (pp. 672-683). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11296 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-05716-9_60