Advertisement

New Approach Based on Texture and Geometric Features for Text Detection

  • Hinde Anoual
  • Sanaa El Fkihi
  • Abdelilah Jilbab
  • Driss Aboutajdine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

Due to the huge amount of data carried by images, it is very important to detect and identify the text region as accurately as possible before performing any character recognition. In this paper we describe a text detection algorithm in complex background. It is based on texture and connected components analysis. First we abstract texture regions which usually contain text. Second, we segment the texture regions into suitable objects; the image is segmented into three classes. Finally, we analyze all connected components present in each binary image according to the three classes with the aim to remove non-text regions. Experiments on a benchmark database show the advantages of the new proposed method compared to another one. Especially, our method is insensitive to complex background, font size and color; and offers high precision (83%) and recall(73%) as well.

Keywords

Text detection text localization feature extraction texture analysis geometric analysis 

References

  1. 1.
    Liu, Y., Goto, S., Ikenaga, T.: A Contour-Based Robust Algorithm for Text Detection in Color Images. IEICE Transactions 89-D(3), 1221–1230 (2006)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Wu, V., Manmatha, R., Riseman, E.M.: Finding text in images. In: DL 1997: Proceedings of the second ACM international conference on Digital libraries, New York, NY, USA, pp. 3–12. ACM, New York (1997)CrossRefGoogle Scholar
  4. 4.
    Chen, D.T., Bourlard, H., Thiran, J.-P.: Text identification in complex background using SVM. In: International Conference on Computer Vision and Pattern Recognition 2001, pp. 621–626 (2001)Google Scholar
  5. 5.
    Ye, Q., Gao, W., Wang, W., Zeng, W.: A robust text detection algorithm in images and video frames. In: Joint Conference of Fourth International Conference on Information Communications and Signal Processing and Pacific-Rim Conference on Multimedia, Singapore (2003)Google Scholar
  6. 6.
    Smith, M.A., Kanade, T.: Video skimming for quick browsing based on audio and image characterization, Carnegie Mellon University, Pittsburgh, PA, Technical Report CMU-CS-95-186 (July 1995)Google Scholar
  7. 7.
    Zhong, Y., Karu, K., Jain, A.K.: Locating text in complex color images. In: ICDAR ’95: Proceedings of the Third International Conference on Document Analysis and Recognition, Washington, DC, USA, vol. 1, p. 146. IEEE Computer Society, Los Alamitos (1995)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K., Yu, B.: Automatic text location in images and video frames. Pattern Recognition 31, 2055–2076 (1998)CrossRefGoogle Scholar
  9. 9.
    David, H.L., Doermann, D., Kia, O.: Automatic text detection and tracking in digital video. IEEE Transactions on Image Processing 9(1) (2000)Google Scholar
  10. 10.
    Zhong, Y., Zhang, H., Jain, A.K.: Automatic caption localization in compressed video. IEEE Trans. Pattern Anal. Mach. Intell. 22(4), 385–392 (2000)CrossRefGoogle Scholar
  11. 11.
    Kim, K.I., Jung, K., Kim, J.H.: Texture-based approach for text detection in image using support vector machine and continuously adaptive mean shift algorithm. IEEE Transaction on PAMI 25, 1631–1639 (2003)Google Scholar
  12. 12.
    Li, X., Wang, W., Jiang, S., Huang, Q., Gao, W.: Fast and effective text detection. In: ICIP, pp. 969–972. IEEE, Los Alamitos (2008)Google Scholar
  13. 13.
    Ye, Q., Jiao, J., Huang, J., Yu, H.: Text detection and restoration in natural scene images. J. Vis. Comun. Image Represent. 18(6), 504–513 (2007)CrossRefGoogle Scholar
  14. 14.
    Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: Towards a system for visually impaired persons. In: ICPR (2), pp. 683–686 (2004)Google Scholar
  15. 15.
    Hanif, S., Prevost, L.: Text detection and localization in complex scene images using constrained adaboost algorithm, pp. 1–5 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hinde Anoual
    • 1
  • Sanaa El Fkihi
    • 1
    • 2
  • Abdelilah Jilbab
    • 1
    • 3
  • Driss Aboutajdine
    • 1
  1. 1.LRIT, unité associée au CNRST, FSRMohammed V University AgdalMorocco
  2. 2.ENSIASMohammed V University SoussiRabatMorocco
  3. 3.Rabat-InstitutsENSET, Madinat AL IrfaneRabatMorocco

Personalised recommendations