Text Detection in Manga by Deep Region Proposal, Classification, and Regression

Wei Ta Chu, Chih Chi Yu

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

1 Citation (Scopus)

Abstract

Text in manga presents high variations and different contextual information, and existing scene text detection methods are not directly applicable. We propose two approaches based on deep networks to detect text in manga. In the first approach, features extracted from multiple CNNs are joined and then fed to a combination of a classification network and a regression network. In the second approach, region proposal, feature extraction, and classification/regression, are taken together in a single deep network. The evaluation results show that the first approach achieves performance comparable to the current state of the art, while the second approach yields a big performance leap over existing ones.

Original languageEnglish
Title of host publicationVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538644584
DOIs
Publication statusPublished - 2018 Jul 2
Event33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018 - Taichung, Taiwan
Duration: 2018 Dec 92018 Dec 12

Publication series

NameVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing

Conference

Conference33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018
CountryTaiwan
CityTaichung
Period18-12-0918-12-12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing

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  • Cite this

    Chu, W. T., & Yu, C. C. (2018). Text Detection in Manga by Deep Region Proposal, Classification, and Regression. In VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing [8698677] (VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VCIP.2018.8698677