Advertisement

Vision-Based Text Segmentation System for Generic Display Units

  • José Carlos Castillo
  • María T. López
  • Antonio Fernández-Caballero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

The increasing use of display units in avionics motivate the need for vision-based text recognition systems to assist humans. The system for generic displays proposed in this paper includes some of the usual text recognition steps, namely localization, extraction and enhancement, and optical character recognition. The proposal has been fully developed and tested on a multi-display simulator. The commercial OCR module from Matrox Imaging Library has been used to validate the textual displays segmentation proposal.

Keywords

Optical Character Recognition Lens Distortion Text Recognition ASCII Character Direct Linear Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdel-Aziz, Y.I., Karara, H.M.: Direct linear transformation into object space coordinates in close-range photogrammetry. In: Proceedings of the Symposium on Close-Range Photogrametry, pp. 1–18 (1971)Google Scholar
  2. 2.
    Andersson, P., von Hofsten, C.: Readability of vertically vibrating aircraft displays. Displays 20, 23–30 (1999)CrossRefGoogle Scholar
  3. 3.
    Chang, F., Chen, G.C., Lin, C.C., Lin, W.H.: Caption analysis and recognition for building video indexing system. Multimedia Systems 10(4), 344–355 (2005)CrossRefGoogle Scholar
  4. 4.
    Faugeras, O.: Three-dimensional computer vision: A geometric viewpoint. MIT Press, Cambridge (1993)Google Scholar
  5. 5.
    Huang, S., Ahmadi, M., Sid-Ahmed, M.A.: A hidden Markov model-based character extraction method. Pattern Recognition (2008), doi:10.1016/j.patcog.2008.03.004Google Scholar
  6. 6.
    Jung, K., Kim, K.I., Jain, A.K.: Text information extraction in images and video: A survey. Pattern Recognition 37, 977–997 (2004)CrossRefGoogle Scholar
  7. 7.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19, 41–47 (1986)CrossRefGoogle Scholar
  8. 8.
    Lienhart, R.: Automatic text recognition in digital videos. In: Proceedings SPIE, Image and Video Processing IV, pp. 2666–2675 (1996)Google Scholar
  9. 9.
    Lin, C.J., Hsieh, Y.-H., Chen, H.-C., Chen, J.C.: Visual performance and fatigue in reading vibrating numeric displays. Displays (2008), doi:10.1016/j.displa.2007.12.004Google Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  11. 11.
    Pikaz, A., Averbuch, A.: Digital image thresholding based on topological stable state. Pattern Recognition 29, 829–843 (1996)CrossRefGoogle Scholar
  12. 12.
    Prewitt, J.M.S., Mendelsohn, M.L.: The analysis of cell images. Annals of the New York Academy of Sciences 128(3), 1035–1053 (1965)CrossRefGoogle Scholar
  13. 13.
    Sato, T., Kanade, T., Hughes, E.K., Smith, M.A., Satoh, S.: Video OCR: indexing digital news libraries by recognition of superimposed caption. ACM Multimedia Systems Special Issue on Video Libraries 7(5), 385–395 (1998)CrossRefGoogle Scholar
  14. 14.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)CrossRefGoogle Scholar
  15. 15.
    Stein, G.P.: Accurate internal camera calibration using rotation with analysis of sources of error. In: Proceedings of the Fifth International Conference on Computer Vision, p. 230 (1995)Google Scholar
  16. 16.
    Stein, G.P.: Lens distortion calibration using point correspondences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 602–608 (1997)Google Scholar
  17. 17.
    Tsai, R.Y.: A versatile camera calibration technique for high accuracy 3-d maching vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics & Automation 3, 323–344 (1987)CrossRefGoogle Scholar
  18. 18.
    Wang, K., Kangas, J.A., Li, W.: Character segmentation of color images from digital camera. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 210–214 (2001)Google Scholar
  19. 19.
    Wang, J., Shi, F., Zhang, J., Liu, Y.: A new calibration model of camera lens distortion. Pattern Recognition 41(2), 607–615 (2008)CrossRefzbMATHGoogle Scholar
  20. 20.
    Wolf, C., Jolion, J.: Extraction and recognition of artificial text in multimedia documents. Pattern Analysis and Applications 6, 309–326 (2003)MathSciNetGoogle Scholar
  21. 21.
    Yan, H., Wu, J.: Character and line extraction from color map images using a multi-layer neural network. Pattern Recognition Letters 15, 97–103 (1994)CrossRefGoogle Scholar
  22. 22.
    Zhu, K., Qi, F., Jiang, R., Xu, L., Kimachi, M., Wu, Y., Aizawa, T.: Using adaboost to detect and segment characters from natural scenes. In: Proceedings of the International Workshop on Camera-based Document Analysis and Recognition, pp. 52–58 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José Carlos Castillo
    • 1
  • María T. López
    • 1
  • Antonio Fernández-Caballero
    • 1
  1. 1.Departamento de Sistemas Informáticos & Instituto de Investigación en Informática de AlbaceteUniversidad de Castilla-La ManchaAlbaceteSpain

Personalised recommendations