Advertisement

Scene Text Extraction from Videos Using Hybrid Approach

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

With fast intensification of existing multimedia documents and mounting demand for information indexing and retrieval, much endeavor has been done on extracting the text from images and videos. The prime intention of the projected system is to spot and haul out the scene text from video. Extracting the scene text from video is demanding due to complex background, varying font size, different style, lower resolution and blurring, position, viewing angle and so on. In this paper we put forward a hybrid method where the two most well-liked text extraction techniques i.e. region based method and connected component (CC) based method comes together. Initially the video is split into frames and key frames obtained. Text region indicator (TRI) is being developed to compute the text prevailing confidence and candidate region by performing binarization. Artificial Neural network (ANN) is used as the classifier and Optical Character Recognition (OCR) is used for character verification. Text is grouped by constructing the minimum spanning tree with the use of bounding box distance.

Keywords

Caption text Preprocessing Scene text Text extraction Text grouping Video frame extraction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pan, Y.-F., Hou, X., Liu, C.-L., Senior Member, IEEE: A Hybrid Approach to Detect and Localize Texts in Natural Scene Images. IEEE Transactions on Image Processing 20(3) (March 2011)Google Scholar
  2. 2.
    Zhao, X., Lin, K.-H., Fu, Y., Member, IEEE, Hu, Y., Member, IEEE, Liu, Y., Member, IEEE, Huang, T.S., Life Fellow, IEEE: Text from Corners: A Novel Approach to Detect Text and Caption in Videos. IEEE Transactions on Image Processing 20(3) (March 2011)Google Scholar
  3. 3.
    Weinman, J., Learned-Miller, E., Hanson, A.: Scene text recognition using similarity and a lexicon with sparse belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 31(10), 1733–1746 (2009)CrossRefGoogle Scholar
  4. 4.
    Tsai, L.W., Hsieh, J.W., Chuang, C.H., Tseng, Y.J., Fan, K.-C., Lee, C.C.: Road Sign Detection Using Eigen ColourGoogle Scholar
  5. 5.
    Nicolas, S., Dardenne, J., Paquet, T., Heutte, L.: Document image segmentation using a 2-D conditional random field model. In: Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR 2007), Curitiba, Brazil, pp. 407–411 (2007)Google Scholar
  6. 6.
    Chen, X.R., Yuille, A.L.: Detecting and Reading Text in Natural Scenes. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, pp. 366–373 (2004)Google Scholar
  7. 7.
    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. IEEE Transaction on. Pattern Anal. Mach. Intell. 25(12), 1631–1639 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lienhart, R., Member, IEEE, Wernicke, A.: Localizing and Segmenting Text in Images and Videos. IEEE Transactions on Circuits and Systems for Video Technology 12(4) (April 2002)Google Scholar
  9. 9.
    Li, H.P., Doermann, D., Kia, O.: Automatic Text Detection and Tracking in Digital Video. IEEE Transaction on Image Processing 9, 147–156 (2000)CrossRefGoogle Scholar
  10. 10.
    Zhong, Y., Zhang, H., Jain, A.K., Fellow: Automatic Caption Localization in Compressed Video. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(4) (April 2000)Google Scholar
  11. 11.
    Zhu, K.H., Qi, F.H., Jiang, R.J., Xu, L., Kimachi, M., Wu, Y., Aizawa, T.: Using Adaboost to Detect and Segment Characters From Natural Scenes. In: Proc. 1st Conf. Caramera Based Document Analysis and Recognition (CBDAR 2005), Seoul, South Korea, pp. 52–59 (2005)Google Scholar
  12. 12.
    Liu, Y.X., Goto, S., Ikenaga, T.: A Contour-based Robust Algorithm for Text Detection in Color Images. IEICE Transaction. Inf. Syst. E89-D(3), 1221–1230 (2006)CrossRefGoogle Scholar
  13. 13.
    Jung, K., Kim, K.I., Jain, A.K.: Text information extraction in images and video: A survey. Pattern Recogn. 37(5), 977–997 (2004)CrossRefGoogle Scholar
  14. 14.
    Niblack, W.: An Introduction to Digital Image Processing. Strandberg Publishing, Birkeroed (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • A. Thilagavathy
    • 1
  • K. Aarthi
    • 1
  • A. Chilambuchelvan
    • 1
  1. 1.Department of Computer Science and EngineeringR.M.K Engineering CollegeKavaraipettaiIndia

Personalised recommendations