Text Extraction from Scene Images Through Local Binary Pattern and Business Features Based Color Image Segmentation

  • Ranjit Ghoshal
  • Anandarup Roy
  • Bibhas Ch. Dhara
  • Swapan K. Parui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)

Abstract

This article proposes a scheme for automatic extraction of text from scene images. First, we apply a color image segmentation algorithm to a scene image. To improve the color image segmentation performance, we incorporate local binary pattern (LBP) and business features within it. Local Binary Pattern (LBP) operator is a texture descriptor for grayscale images. On the other hand, the business feature describes the variation in intensity. The segmentation procedure separates out certain homogenous connected components from the image. We next inspect these connected components in order to identify possible text components. Here, we define a number of shape based features that distinguish between text and non-text connected components. Our experiments are based on the ICDAR 2011 Born Digital data set. The experimental results are satisfactory.

Keywords

Scene image Color image segmentation Local binary pattern Text identification 

References

  1. 1.
    Wu, V., Manmatha, R., Riseman, E.M.: Textfinder: an automatic system to detect and recognize text in images. IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1224–1229 (1999)CrossRefGoogle Scholar
  2. 2.
    Tsai, C., Lee, H.: Binarization of color document images via luminance and saturation color features. IEEE Trans. Image Process. 11(4), 434–451 (2002)CrossRefGoogle Scholar
  3. 3.
    Jung, K., Kim, I.K., Kurata, T., Kourogi, M., Han, H.J.: Text scanner with text detection technology on image sequences. In: Proceedings of International Conference on Pattern Recognition, vol. 3, pp. 473–476 (2002)Google Scholar
  4. 4.
    Gllavata, J., Ewerth, R., Freisleben, B.: Text detection in images based on unsupervised classification of high frequency wavelet coefficients. In: Proceedings of International Conference on Pattern Recognition, vol.1, pp. 425–428 (2004)Google Scholar
  5. 5.
    Bhattacharya, U., Parui, S.K., Mondal, S.: Devanagari and Bangla text extraction from natural scene images. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 171–175 (2009)Google Scholar
  6. 6.
    Mandal, A.K., Pal, S., De, A.K., Mitra, S.: Novel approach to identify good tracer clouds from a sequence of satellite images. IEEE Trans. Geosci. Remote Sensing. 43(4), 813–818 (2005)CrossRefGoogle Scholar
  7. 7.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 1224–1229 (2002)CrossRefMATHGoogle Scholar
  8. 8.
    Ghoshal, R., Roy, A., Bhowmik, T.K., Parui, S.K.: Decision tree based recognition of Bangla text from outdoor scene images. In: Proceedings of the 18th International Conference on Neural Information Processing, pp. 538–546 (2011)Google Scholar
  9. 9.
    Karatzas, D., Robles Mestre, S., Mas, J., Nourbakhsh, F., Roy, P.P.: ICDAR 2011 robust reading competition-challenge 1: reading text in born-digital images (web and email). In: Proceedings of 11th International Conference of Document Analysis and Recognition (2011)Google Scholar
  10. 10.
    Clavelli, A., Karatzas, D., Lladós, J.: A framework for the assessment of text extraction algorithms on complex colour images. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS’10. ACM, pp. 19–26 (2010)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Ranjit Ghoshal
    • 1
  • Anandarup Roy
    • 2
  • Bibhas Ch. Dhara
    • 3
  • Swapan K. Parui
    • 2
  1. 1.St. Thomas’ College of Engineering and TechnologyKolkataIndia
  2. 2.CVPR UnitIndian Statistical InstituteKolkataIndia
  3. 3.IT DepartmentJadavpur UniversityKolkataIndia

Personalised recommendations