Skip to main content
Log in

A video text location method based on background classification

  • Regular Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

In this paper, we propose a simple yet powerful video text location scheme. Firstly, an edge-based background classification is applied to the input video frames, which are subsequently classified into three categories: simple, normal and complex. Then, for the three different types of video frames, different text location methods are adopted, respectively: for the simple background class, a stroke-based text location scheme is used; for the normal background class, a variant of morphology called conditional morphology is incorporated to remove the non-text noises; for the complex background situation, after location routine based on stroke analysis and conditional morphology, an SVM text detector is trained to reduce the false alarms. Experimental results show that our approach performs well in various videos with high speed and precision.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen D., Odobez J.-M., Bourlard H.: Text detection and recognition in images and video frames. Pattern Recognit. 37, 595–608 (2004)

    Article  Google Scholar 

  2. Chen D., Odobez J.-M., Thiran J.-P.: A localization/verification scheme for finding text in images and video frames based on contrast independent features and machine learning methods. Signal Process.: Image Commun. 19, 205–217 (2004)

    Article  Google Scholar 

  3. Chuang L., Ding X., Wu Y.: An algorithm for text location in images based on histogram features and AdaBoost. J. Image Graph. 11(3), 325–331 (2006)

    Google Scholar 

  4. Dubey P.: Edge based text detection for multi-purpose application. 8th InternationalConference on Signal Processing 4, 16–20 (2006)

    Google Scholar 

  5. Freund Y., Schapire R.E.: A decision-theoretic generalization of online learning and an application to boosting. J Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  6. Gonzalez R.C., Woods R.E.: Digital Image Processing, pp. 420–454. Publishing House of Electronic Industry, Beijing (2005)

    Google Scholar 

  7. Hua, X.-S., Chen, X.-R., Wenyin, L., Zhang, H.-J.: Automatic Location of Text in Video Frames, Proceedings of the 2001 ACM Workshops on Multimedia: Multimedia Information Retrieval, pp. 24–27 (2001)

  8. Jain A.K., Vailaya A.: Images retrieval using color and shape. Pattern Recognit. 29(8), 1233–1244 (1996)

    Article  Google Scholar 

  9. Jung K., Kim K.I., Jain A.K.: Text information extraction in images and video: a survey. Pattern Recognit. 37, 977–997 (2004)

    Article  Google Scholar 

  10. Kim K.I., Jung K., Kim J.H.: Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. Pattern Anal. Mach. Learn. 26, 1631–1639 (2003)

    Google Scholar 

  11. Lienhart, R.: OCR, Video: A Survey and Practitioner’s Guide. Intel Corporation, Microprocessor Research Labs, Santa Clara, California (2003)

  12. Lienhart R., Wernicke A.: Localizing and segmenting text in images and videos. IEEE Trans. Circuits Syst. Video Technol. 12, 256–268 (2002)

    Article  Google Scholar 

  13. Liu C., Wang C., Dai R.: Text detection in images based on unsupervised classification of edge-based features. Proceedings of the 8th International Conference on Document Analysis and Recognition 2, 607–612 (2005)

    Google Scholar 

  14. Miao, G., Huang, Q., Jiang, S., Gao, W.: Coarse-to-fine Video Text Detection, 2008 IEEE Int. Conf. Multimed. Expo. 569–572 (2008)

  15. Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man, Cybern. 9, 62–66 (1979)

    Article  Google Scholar 

  16. Platt J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization, pp. 185–208. MIT Press, USA (1999)

    Google Scholar 

  17. Raju S.S., Pati P.B., Ramakrishnan A.G.: Text Localization and Extraction from Complex Color Images, Lecture Notes in Computer Science, pp. 486–493. Springer, Berlin (2005)

    Google Scholar 

  18. Shin C.S., Kim K.I., Park M.H., Kim H.J.: Support vector machine-based text detection in digital video. Proceedings of the IEEE Signal Proceesing Society Workshop on Neural Networks for Sunal Processing, 2, 634–641 (2000)

    Google Scholar 

  19. Sun H., Zhao N., Xu X.: Extraction of text under complex background using wavelet transform and support vector machine. Proceedings of 2006 International Conference on Mechatronics and Automation 2, 1493–1497 (2006)

    Article  Google Scholar 

  20. Vapnik V.: Statistical Learning Theory, pp. 421–440. Wiley, USA (1998)

    MATH  Google Scholar 

  21. Ye X., Cheriet M., Suen C.Y.: Stroke-model-based character extraction from gray-level document images. IEEE Trans. Image Process. 10, 1152–1161 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  22. Ye Q., Huang Q., Gao W., Zhao D.: Fast and robust text detection in images and video frames. Image Vis. Comput. 23, 565–576 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiufei Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, X., Huang, L. & Liu, C. A video text location method based on background classification. IJDAR 13, 173–186 (2010). https://doi.org/10.1007/s10032-009-0104-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-009-0104-x

Keywords

Navigation