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Text detection in scene images based on feature detection and tensor voting

Published:08 January 2015Publication History

ABSTRACT

In this paper, a novel approach based on feature points to localize text in natural scene images is proposed. The key idea of this approach is: feature point detection technique is used to extract the corner points of edges in connected components, to select candidate regions. Then, the candidate regions are verified by tensor voting, to extract perceptual structures from noisy data. Finally, non-text regions are filtered out by using regions area, orientation, and aspect ratio. The experiment results demostrate the performance of our approach.

References

  1. Jung, K., Kim, K. I., and Jain, A. K. 2004. Text Information extraction in images and video: A survey. Pattern Recognition, Vol. 37, (May 2004), 977--997.Google ScholarGoogle Scholar
  2. Toan, N. D., Park, J., and Lee, G. 2010. Using 2D tensor voting in text detection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Mar. 2010), 818--821.Google ScholarGoogle Scholar
  3. Tomasi, C. and Manduchi, R. 1998. Bilateral Filtering for Gray and Color Images. In Proceedings of the Sixth International Conference on Computer Vision (ICCV '98). IEEE Computer Society, Washington, DC, USA, 839--846. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Förstner, W. and Gülch, E. 1987. A fast operator for detection and precise location of distinct point, corners and centres of circular features. In Proceedings of the ISPRS Conference on Fast Processing of Photogrammetric Data, (Switzerland, Interlaken), 281--305.Google ScholarGoogle Scholar
  5. Schmid, C., Mohr, R. and Bauckhage, C. 2000. Evaluation of Interest Point Detectors. Int. J. Comput. Vision, 37, 2, 151--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Förstner, W. and Gülch, E. 1897. A fast operator for detection and precise location of distinct point, corners and centres of circular features. In Proceedings of the ISPRS Conference on Fast Processing of Photogrammetric Data, Switzerland, Interlaken, 281--305.Google ScholarGoogle Scholar
  7. Belongie, S. 2000. Notes on Corner Detection.Google ScholarGoogle Scholar
  8. Medioni, G., Lee, M. S., and Tang, C. K. 2000. A Computational Framework for Segmentation and Grouping. Elsevier, Amsterdam. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Tong, W. S., Tang, C. K., and Medioni, G. 2001. First Order Tensor Voting, and Application to 3-D Scale Analysis. Proc. CVPR, 175--182.Google ScholarGoogle Scholar
  10. ICDAR 2003, http://algoval.essex.ac.uk/icdar/Datasets.htmlGoogle ScholarGoogle Scholar
  11. Samarabandu, J. and Liu, X. P. 2006. An edge-based text region extraction algorithm for indoor mobile robot navigation. International Journal of Signal Processing, 273--280.Google ScholarGoogle Scholar
  12. Xiaoqing, L. and Jagath, S. 2006. Multiscale Edge-Based Text Extraction from Complex Images. ICME, (Jul. 2016), 1721--1724.Google ScholarGoogle Scholar
  13. Julinda, G., Ralph, E., and Bernd, F. 2003. A Robust algorithm for Text detection in images. Proceedings of the 3rd international symposium on Image and Signal Processing and Analysis, (Sept. 2003), 611--616.Google ScholarGoogle Scholar

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  1. Text detection in scene images based on feature detection and tensor voting

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    • Published in

      cover image ACM Conferences
      IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication
      January 2015
      674 pages
      ISBN:9781450333771
      DOI:10.1145/2701126

      Copyright © 2015 ACM

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      New York, NY, United States

      Publication History

      • Published: 8 January 2015

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      Overall Acceptance Rate213of621submissions,34%

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