Texture-Based Text Detection in Digital Images with Wavelet Features and Support Vector Machines

  • Marcin Grzegorzek
  • Chen Li
  • Johann Raskatow
  • Dietrich Paulus
  • Natalia Vassilieva
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)


In this paper we propose to combine region-based and texture-based approaches for text detection in digital images. Our solution is based on a cascade filtering of image regions. First, we apply heuristic filtering to disregard certain non-textual areas. Second, we perform a more precise and expensive texture-based filtering using support vector machines and wavelet-based texture features. We have evaluated our approach with the ICDAR 2003 text locating competition benchmark collection and tools. The experimental results showed competitive performance of our solution by means of recall and precision compared to other text detection approaches participated in ICDAR 2003 and lower computational cost at the same time.


Text Detection Wavelet Features Support Vector Machines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, D.T., Odobez, J.M., Bourlard, H.: Text detection and recognition in images and video frames 37(3), 595–608 (2004)Google Scholar
  2. 2.
    Chen, X., Yuille, A.L.: Detecting and reading text in natural scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 336–373 (2004)Google Scholar
  3. 3.
    Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2963–2970 (June 2010)Google Scholar
  4. 4.
    Farhoodi, R., Kasaei, S.: Abstract text segmentation from images with textured ans colored background (2008)Google Scholar
  5. 5.
    Gllavata, J.: Extracting Textual Information from Images and Videos for Automatic Content-Based Annotation and Retrieval. Dissertation, Fachbereich Mathematik und Informatik der Philipps-Universitaet Marburg (2007)Google Scholar
  6. 6.
    Jiang, R., Qi, F., Xu, L., Wu, G.: Detecting and segmenting text from natural scenes with 2-stage classification. In: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, ISDA 2006, pp. 819–824. IEEE Computer Society, Washington, DC (2006)CrossRefGoogle Scholar
  7. 7.
    Jolliffe, I.T.: Principal Component Analysis. Springer (1986),
  8. 8.
    Jung, K., Kim, K.I., Jain, A.K.: Text information extraction in images and video: a survey. Pattern Recognition 5, 977–997 (2004)CrossRefGoogle Scholar
  9. 9.
    Lucas, S.M., Panaretos, A., Sosa, L., Wong, A.T.S., Ashida, K., Nagai, H., Okamoto, M., Yamamoto, H., Miyao, H., Zhu, J., Ou, W., Wolf, C., Jolion, J.M., Todoran, L., Worring, M., Lin, X.: X.: Icdar 2003 robust reading competitions: entries, results and future directions. International Journal on Document Analysis and Recognition - Special Issue on Camera-based Text and Document Recognition 7(2-3), 105–122 (2005)CrossRefGoogle Scholar
  10. 10.
    Hiremath, P.S., Shivashankar, S.: Wavelet based features for texture classification. ICGST International Journal on Graphics, Vision and Image Processing 6, 55–58 (2006)Google Scholar
  11. 11.
    Tadeusiewicz, R.: How Intelligent Should Be System for Image Analysis? In: Kwasnicka, H., Jain, L.C. (eds.) Innovations in Intelligent Image Analysis. SCI, pp. VX. Springer, Heidelberg (2011),
  12. 12.
    Shim, J.-C., Dorai, C., Bolle, R.: Automatic text extraction from video for content-based annotation and retrieval. In: ICPR 1998: Proceedings of the 14th International Conference on Pattern Recognition, vol. 1, p. 618. IEEE Computer Society, Washington, DC (1998)Google Scholar
  13. 13.
    Tadeusiewicz, R.: What does it means ”automatic understanding of the images”? In: Proceedings of the 2007 IEEE International Workshop on Imaging Systems and Techniques, pp. 1–3 (May 2007)Google Scholar
  14. 14.
    Wolf, C., Jolion, J.-M.: Object count/area graphs for the evaluation of object detection and segmentation algorithms. International Journal on Document Analysis and Recognition 8(4), 280–296 (2006)CrossRefGoogle Scholar
  15. 15.
    Wu, V., Manmatha, R.: andE.M. Riseman. Finding text in images. In: ACM International Conference on Digital libraries (DL), pp. 3–12 (1997)Google Scholar
  16. 16.
    Ye, Q.X., Huang, Q.M., Gao, W., Zhao, D.B.: Fast and robust text detection in images and video frames 23(6), 565–576 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Marcin Grzegorzek
    • 1
  • Chen Li
    • 1
  • Johann Raskatow
    • 2
  • Dietrich Paulus
    • 2
  • Natalia Vassilieva
    • 3
  1. 1.Pattern Recognition GroupUniversity of SiegenSiegenGermany
  2. 2.Active Vision GroupUniversity of Koblenz-LandauLandauGermany
  3. 3.HP LabsSt. PetersburgRussia

Personalised recommendations