An Exploration of Wavelet Transform and Level Set Method for Text Detection in Images and Video Frames

  • V. N. Manjunath Aradhya
  • M. S. Pavithra
  • S. K. Niranjan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)


In texture-based text detection method, text regions are detected by obtaining textural properties of an image. In order to obtain textural properties of an input image, the proposed system performs single-level 2D DWT. The resultant detail coefficients are averaged to get a better texture properties and to localize for further processing. Then, 2D DWT is explored with a level set method to address the problem of text detection especially curving portions of text present in images and video frames. Thus, the proposed system implements the level set method to detect the true text regions effectively based on contours in images and video frames. Experimental results prove that the proposed level set based method is competitive when compared with other existing methods in reducing false positive rate and mis detection rate. Hence, the proposed system is encouraging and useful to carry out further research on text extraction in images and video.


Single-Level 2D DWT Level Set Method Text Detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Meng, L., Cai, Y., Wang, M., Li, Y.: TV Commercial Detection Based on Shot Change and Text Extraction, pp. 10–13. IEEE (2009)Google Scholar
  2. 2.
    Shivakumara, P., Phan, T.Q., Tan, C.L.: New Wavelet and Color Features for Text Detection in Video. In: Proceedings of International Conference on Pattern Recognition, pp. 3996–3999 (2010)Google Scholar
  3. 3.
    Sharma, N., Shivakumara, P., Pal, U., Blumenstein, M., Tan, C.L.: A New Method for Arbitrarily-Oriented Text Detection in Video. In: Proceedings of 10th IAPR International Workshop on Document Analysis Systems, pp. 74–78 (2012)Google Scholar
  4. 4.
    Shivakumara, P., Sreedhar, R.P., Phan, T.Q., Lu, S., Tan, C.L.: Multioriented Video Scene Text Detection Through Bayesian Classification and Boundary Growing. IEEE Transactions on Circuits and Systems for Video Technology 22, 1227–1235 (2012)CrossRefGoogle Scholar
  5. 5.
    Yi, X.G.: Automatic Caption Extraction of News Video and its Implementation, pp. 122–125 (2012)Google Scholar
  6. 6.
    Polikar, R.: The Wavelet Tutorial part IV, Multiresolution Analysis:The Discrete Wavelet Transform. Rowan University (2004), Available via DIALOG
  7. 7.
    Tsai, R., Osher, S.: Level Set Methods and Their Applications in Image Science. Communications in Mathematical Sciences 1(4), 1–20 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Zhang, K., Zhang, L., Song, H., Zhou, W.: Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing 28, 668–676 (2010)CrossRefGoogle Scholar
  10. 10.
    Phan, T., Shivakumara, P., Tan, C.: A Laplacian method for video text detection. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 66–70 (2009)Google Scholar
  11. 11.
    Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting Texts of Arbitrary Orientations in Natural Images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1083–1090 (2012)Google Scholar
  12. 12.
    Liu, C., Wang, C., Dai, R.: Text detection in images based on unsupervised classification of edge-based features. In: Proceedings of ICDAR, pp. 610–614 (2005)Google Scholar
  13. 13.
    Aradhya, V.N.M., Pavithra, M.S., Naveena, C.: A robust multilingual text detection approach based on transforms and wavelet entropy. In: Proceedings of 2nd International Conference on Computer, Communication, Control and Information Technology(C3IT 2012), Procedia Technology, pp. 232–237. Elsevier (2012)Google Scholar
  14. 14.
    Manjunath Aradhya, V.N., Pavithra, M.S.: An application of K-means clustering for improving video text detection. In: Abraham, A., Thampi, S.M. (eds.) Intelligent Informatics. AISC, vol. 182, pp. 41–47. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • V. N. Manjunath Aradhya
    • 1
  • M. S. Pavithra
    • 2
  • S. K. Niranjan
    • 1
  1. 1.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia
  2. 2.Department of MCADayananda Sagar College of EngineeringBangaloreIndia

Personalised recommendations