Extracting Text Information for Content-Based Video Retrieval

  • Lei Xu
  • Kongqiao Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4903)

Abstract

In this paper we present a novel video text detection and segmentation system. In the detection stage, we utilize edge density feature, pyramid strategy and some weak rules to search for text regions, so that high detection rate can be achieved. Meanwhile, to eliminate the false alarms and improve the precision rate, a multilevel verification strategy is adopted. In the segmentation stage, a precise polarity estimation algorithm is firstly provided. Then, multiple frames containing the same text are integrated to enhance the contrast between text and background. Finally, a novel connected components based binarization algorithm is proposed to improve the recognition rate. Experimental results show the superior performance of the proposed system.

Keywords

False Alarm Recognition Rate Local Binary Pattern Text Line Optical Character Recognition 
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.
    Aslandogan, Y., Yu, C.T.: Techniques and Systems for Image and Video Retrieval. IEEE Transactions on Knowledge and Data Engineering 11(1), 56–63 (1999)CrossRefGoogle Scholar
  2. 2.
    Gllavata, J., Ewerth, R., Freisleben, B.: Text Detection in Images Based on Unsupervised Classification of High-frequency Wavelet Coefficients. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 1, pp. 425–428 (2004)Google Scholar
  3. 3.
    Tekinalp, S., Alatan, A.: Utilization of Texture, Contrast and Color Homogeneity for Detecting and Recognizing Text from Video Frames. In: Proceedings of 2003 International Conference on Image Processing, vol. 2, pp. 505–508 (2003)Google Scholar
  4. 4.
    Kim, K., Byun, H., Song, Y., Choi, Y., Chi, S., Kim, K., Chung, Y.: Scene Text Extraction in Natural Scene Images Using Hierarchical Feature Combining and Verification. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 2, pp. 679–682 (2004)Google Scholar
  5. 5.
    Wang, K., Kangas, J.A.: Character Location in Scene Images from Digital Camera. Pattern Recognition 36(10), 2287–2299 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    Cai, M., Song, J., Lyu, M.R.: A New Approach for Video Text Detection. In: Proceedings of 2002 International Conference on Image Processing, vol. 1, pp. 117–120 (2002)Google Scholar
  7. 7.
    Wang, R., Jin, W., Wu, L.: A Novel Video Caption Detection Approach Using Multi-frame Integration. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 1, pp. 449–452 (2004)Google Scholar
  8. 8.
    Wolf, C., Jolion, J.: Extraction and Recognition of Artificial Text in Multimedia Documents. Pattern Analysis and Application 6(4), 309–326 (2003)MathSciNetGoogle Scholar
  9. 9.
    Hua, X.S., Yin, P., Zhang, H.J.: Efficient Video Text Recognition Using Multiple Frame Integration. In: Proceedings of 2002 International Conference on Image Processing, vol. 2, pp. 397–400 (2002)Google Scholar
  10. 10.
    Lienhart, R., Wernicke, A.: Localizing and Segmenting Text in Images and Videos. IEEE Transactions on Circuits and Systems for Video Technology 12(4), 256–268 (2002)CrossRefGoogle Scholar
  11. 11.
    Hasan, Y.M., Karam, L.J.: Morphological Text Extraction from Images. IEEE Transaction on Image Processing 9(11), 1978–1983 (2000)CrossRefGoogle Scholar
  12. 12.
    Strouthopoulos, C., Papamarkos, N., Atsalakis, A.: Text Extraction in Complex Color Documents. Pattern Recognition 35(8), 1743–1758 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Ahonen, T., Hadid, A., Pietikinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Hadid, A., Pietikainen, M., Ahonen, T.: A Discriminative Feature Space for Detecting and Recognizing Faces. In: Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 797–804 (2004)Google Scholar
  15. 15.
    Jung, B.H., Katagiri, S.: Discriminative Learning for Minimum Error Classification. IEEE Transaction on Signal Processing 40(12), 3043–3054 (1992)CrossRefGoogle Scholar
  16. 16.
    Otsu, N.: A Threshold Selection Method from Gray-level Histograms. Man and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Seeger, M., Dance, C.: Binarising Camera Images for OCR. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, vol. 1, pp. 54–58 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lei Xu
    • 1
    • 2
  • Kongqiao Wang
    • 1
  1. 1.System Research Center, Beijing, Nokia Research Center 
  2. 2.Beijing University of Posts and Telecommunications 

Personalised recommendations