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Text Localization with Hierarchical Multiple Feature Learning

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Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

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Abstract

In this paper, we focus on English text localization in natural scene images. We propose a hierarchical localization framework which goes from characters to strings to words. Different from existing methods which either bet on sophisticated hand-crafted features or rely on heavy learning models, our approach tends to design simple but effective features and learning models. In this study, we introduce a kind of two level character structure features in collaboration with the Histogram of Gradient (HOG) and the Convolutional Neural Network (CNN) features for character localization. In string localization, a nine-dimension string feature is proposed for discriminative verification after grouping characters. For the final word localization, we learn an optimal splitting strategy based on the interval cues to split strings into words. Experiments on the challenging ICDAR benchmark datasets demonstrate the effectiveness and superiority of our approach.

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Acknowledgments

This work was supported by the National Natural Science Foundations of China under Grants 61373077, 61472334 and 61170179, the Natural Science Foundation of Fujian Province of China Under Grant 2013J01257, the Fundamental Research Funds for the Central Universities under Grant 20720130720, the 2014 national college students’ innovative and entrepreneurial training project, and the Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology YKJ12023R.

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Authors and Affiliations

  1. Computer Science Department, Xiamen University, Xiamen, China

    Yanyun Qu, Li Lin, Weiming Liao, Junran Liu & Hanzi Wang

  2. Center for Frontier Science and Technology, Nara Institute of Science Technology, Nara, Japan

    Yang Wu

Authors
  1. Yanyun Qu
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  2. Li Lin
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  3. Weiming Liao
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  4. Junran Liu
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  5. Yang Wu
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  6. Hanzi Wang
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Corresponding author

Correspondence to Yanyun Qu .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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© 2015 Springer International Publishing Switzerland

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Qu, Y., Lin, L., Liao, W., Liu, J., Wu, Y., Wang, H. (2015). Text Localization with Hierarchical Multiple Feature Learning. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_61

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  • DOI: https://doi.org/10.1007/978-3-319-24075-6_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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