An Application of K-Means Clustering for Improving Video Text Detection

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


In the present work, we explore an extensive applications of Gabor filter and K-means clustering algorithm in detection of text in an unconstrained complex background and regular images. The system is a comprehensive of four stages: In the first stage, combination of wavelet transforms and Gabor filter is applied to extract sharpened edges and textural features of a given input image. In the second stage, the resultant Gabor output image is grouped into three clusters to classify the background, foreground and the true text pixels using K-means clustering algorithm. In the third stage of the system, morphological operations are performed to obtain connected components, then after a concept of linked list approach is in turn used to build a true text line sequence. In the final stage, wavelet entropy is imposed on an each connected component sequence, in order to determine the true text region of an input image. Experiments are conducted on 101 video images and on standard ICDAR 2003 database. The proposed method is evaluated by testing the 101 video images as well with the ICDAR 2003 database. Experimental results show that the proposed method is able to detect a text of different size, complex background and contrast. Withal, the system performance outreaches the existing method in terms of detection accuracy.


Wavelet Transform Gabor filter K-means clustering linked list approach Wavelet Entropy 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chowdhury, S.P., Dhar, S., Das, A.K., Chanda, B., McMenemy, K.: Robust Extraction of Text from Camera Images. In: The Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1280–1284 (2009)Google Scholar
  2. 2.
    Manjunath Aradhya, V.N., Pavithra, M.S., Naveena, C.: A Robust Multilingual Text Detection Approach Based on Transforms and Wavelet Entropy. In: The Proceedings of Procedia Technology (Elsevier) 2nd International Conference on Computer, Communication, Control and Information Technology (C3IT-2012), Hooghly, West Bengal, India (in press, 2012)Google Scholar
  3. 3.
    Kaushik, N., Sarathi, D., Mittal, A.: Robust Text Detection in images using morphological operations and Gabor wavelet. In: The Proceedings of EAIT, pp. 153–156 (2006)Google Scholar
  4. 4.
    Phan, T., Shivakumara, P., Tan, C.: A Laplacian method for video text detection. In: The Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 66–70 (2009)Google Scholar
  5. 5.
    Shivakumara, P., Phan, T., Tan, C.: A Robust Wavelet Transform Based Technique for Video Text Detection. In: The Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1285–1289 (2009)Google Scholar
  6. 6.
    Shivakumara, P., Phan, T., Tan, C.: A Laplacian Approach to Multi-Oriented Text Detection in Video. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 412–419 (2011)CrossRefGoogle Scholar
  7. 7.
    Naveena, C., Manjunath Aradhya, V.N.: A linked List Approach for Handwritten Textline Segmentation. Journal of Intelligent Systems (in press, 2012)Google Scholar
  8. 8.
    Liu, C., Wang, C., Dai, R.: Text Detection in Images Based on Unsupervised Classification of Edge-based Features. In: ICDAR, pp. 610–614 (2005)Google Scholar
  9. 9.
    Wong, E.K., Chen, M.: A new robust algorithm for video text extraction. In: The Proceedings of First Asian Conference on Pattern Recognition, ACPR, vol. 36, pp. 1397–1406 (2003)Google Scholar
  10. 10.
    Mariano, V.Y., Kasturi, R.: Locating Uniform Colored Text in Video Frames. In: 15th ICPR, vol. 4, pp. 539–542 (2000)Google Scholar
  11. 11.
    Teknomo, K.: Kardi Teknomo’s Tutorials (2006); Available via Kardi Teknomo,
  12. 12.
    Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 Robust Reading competitions. In: ICDAR 2003, pp. 682–687 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringDayananda Sagar College of EngineeringBangaloreIndia
  2. 2.Department of Master of Computer ApplicationsDayananda Sagar College of EngineeringBangaloreIndia

Personalised recommendations