Detecting and Recognizing LED Dot Matrix Text in Natural Scene Images

  • Wahyono
  • Kang-Hyun Jo
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


This paper addresses a method for light-emitting diode (LED) dot matrix text detection and recognition in natural scene images. Unlike general text detection and recognition, the LED text detection is quite difficult to be done due to discontinuous character. In our proposed method, first, the Canny edge detector is applied to produce an edge image. From the edge image, the interesting points representing the center of a blob are extracted. These interesting points then are merged based on their properties to generate a character component. Through feature-based template matching, the filtering and recognizing process are performed simultaneously. Experimental results show that the proposed method is reliable, effective and fast to detect and recognize the LED text in natural scene images which general text method does not cover.


LED dot matrix text detection and recognition feature-based template matching 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Huang, W.F.: Designing a display unit to drive the 8x8 dot-matrix display. In: IEEE 5th International Nanoelectronics Conferences (INEC), pp. 385–388 (2013)Google Scholar
  2. 2.
    Neumann, L., Matas, J.: A method for text localization and recognition in real-world images. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 770–783. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Wang, K., Babenko, B., Belongie, S.: End-to-End Scene Text Recognition. In: International Conference on Computer Vision, ICCV (2011)Google Scholar
  4. 4.
    Yi, J., Peng, Y., Xiao, J.: Color-based clustering for text detection and extraction in image. In: 15th International Conference on Multimedia (2007)Google Scholar
  5. 5.
    Liu, C., Wang, C.-H., Dai, R.-W.: Text Detection in Images Based on Color Texture Features. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005, Part I. LNCS, vol. 3644, pp. 40–48. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Epshtein, J., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proc. CVPR (2010)Google Scholar
  7. 7.
    Zhang, J., Kasturi, R.: Text detection using edge gradient and graph spectrum. In: International Conference on Pattern Recognition (2010)Google Scholar
  8. 8.
    Cueevas, E., et al.: Fast algorithm for multiple-circle detection on images using learning automata. IET Image Processing (2012)Google Scholar
  9. 9.
    Canny, J.: A Computational Approach To Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–714 (1986)CrossRefGoogle Scholar
  10. 10.
    He, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Transaction on Image Processing 17(5), 749–756 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 3538–3545 (2012)Google Scholar
  12. 12.
    Yi, S., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Transactions on Image Processing 20(9), 2594–2605 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Weinman, J.J., Learned-Miller, E., Hanson, A.R.: Scene text recognition using similarity and a lexicon with sparse belief propagation. IEEE Transaction on Pattern Analysis and Machine Inteligence 31, 1733–1746 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wahyono
    • 1
  • Kang-Hyun Jo
    • 1
  1. 1.Intelligent Systems Lab., Graduate School of Electrical EngineeringUniversity of UlsanUlsanKorea

Personalised recommendations