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Scene Text Segmentation Based on Local Image Phase Information and MSER Method

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Pattern Recognition (MCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10880))

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Abstract

The objective of text segmentation algorithms is a pixel-level separation of characters from the image background. This task is difficult due to several factors such as environmental aspects, image acquisition problems, and complex textual content. Up to now, the MSER technique has been widely used to solve the problem due to its invariance to geometric distortions, robustness to noise and illumination variations. However, when pixels intensities are too low, the MSER method often fails. In this paper, a new text segmentation method based on local phase information is proposed. Phase-based stable regions are obtained while the phase congruency values are used to select candidate regions. The computer simulation results show the robustness of the proposed method to different image degradations. Moreover, the method outperforms the MSER technique in most of the cases.

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Notes

  1. 1.

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  2. 2.

    http://rrc.cvc.uab.es/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, CICESE, 22860, Ensenada, BC, Mexico

    Julia Diaz-Escobar & Vitaly Kober

Authors
  1. Julia Diaz-Escobar
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  2. Vitaly Kober
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Corresponding author

Correspondence to Julia Diaz-Escobar .

Editor information

Editors and Affiliations

  1. National Institute of Astrophysics, Optics and Electronics, Sta. Maria Tonantzintla, Puebla, Mexico

    José Francisco Martínez-Trinidad

  2. National Institute of Astrophysics, Optics and Electronics, Sta. Maria Tonantzintla, Puebla, Mexico

    Jesús Ariel Carrasco-Ochoa

  3. Autonomous University of Puebla, Puebla, Puebla, Mexico

    José Arturo Olvera-López

  4. University of South Florida, Tampa, Florida, USA

    Sudeep Sarkar

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Diaz-Escobar, J., Kober, V. (2018). Scene Text Segmentation Based on Local Image Phase Information and MSER Method. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-92198-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92197-6

  • Online ISBN: 978-3-319-92198-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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