Methods for Written Ancient Music Restoration

  • Pedro Castro
  • J. R. Caldas Pinto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)


Degradation in old documents has been a matter of concern for a long time. With the easy access to information provided by technologies such as the Internet, new ways have arisen for consulting those documents without exposing them to yet more dangers of degradation. While restoration methods are present in the literature in relation to text documents and artworks, little attention has been given to the restoration of ancient music. This paper describes and compares different methods to restore images of ancient music documents degraded over time. Six different methods were tested, including global and adaptive thresholding, color clustering and edge detection. In this paper we conclude that those based on the Sauvola’s thresholding algorithm are the better suited for our proposed goal of ancient music restoration.


Ancient Music Restoration Image Processing Document Degradation Thresholding Clustering Edge Detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baird, H.: The state of the art of document image degradation modeling (2000)Google Scholar
  2. 2.
    Drira, F.: Towards restoring historic documents degraded over time. In: Document Image Analysis for Libraries, pp. 350–357 (2006)Google Scholar
  3. 3.
    Stanco, F., Ramponi, G.: Detection of Water Blotches in Antique Documents. In: Proc. 8th COST 276 Workshop, Trondheim, Norway (May 2005)Google Scholar
  4. 4.
    Liu, Y., Srihari, S.N.: Document image binarization based on texture features. IEEE Trans. Pattern Anal. Mach. Intell 19(5), 540–544 (1997)CrossRefGoogle Scholar
  5. 5.
    Liang, S., Ahmadi, M., Shridhar, M.: A morphological approach to text string extraction from regular periodic overlapping text/background images. ICIP (1), 144–148 (1994)Google Scholar
  6. 6.
    Yang, Y., Yan, H.: An adaptive logical method for binarization of degraded document images. Pattern Recognition 33, 787–807 (2000)CrossRefGoogle Scholar
  7. 7.
    Trier, Ø.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Mach. Intell 17(12), 1191–1201 (1995)Google Scholar
  8. 8.
    Taxt, T., Trier, O.D.: Evaluation of binarization methods for document images. IEEE Trans. Pattern Analysis and Machine Intelligence 17(6), 640–640 (1995)Google Scholar
  9. 9.
    Negishi, H., Kato, J., Hase, H., Watanabe, T.: Character extraction from noisy background for an automatic reference system. In: ICDAR, pp. 143–146 (1999)Google Scholar
  10. 10.
    Gatos, B., Pratikakis, I., Perantonis, S.J.: An adaptive binarization technique for low quality historical documents. In: Workshop on Document Analysis Systems, pp. 102–113 (2004)Google Scholar
  11. 11.
    Leedham, G., Varma, S., Patankar, A., Govindaraju, V.: Separating text and background in degraded document images: A comparison of global thresholding techniques for multi-stage thresholding. In: Frontiers in Handwriting Recognition, pp. 244–249 (2002)Google Scholar
  12. 12.
    Garain, U., Paquet, T., Heutte, L.: On foreground – background separation in low quality document images. International Journal on Document Analysis and Recognition 8(1), 47–63 (2000)Google Scholar
  13. 13.
    He, J., Do, Q.D.M., Downton, A.C., Kim, J.H.: A comparison of binarization methods for historical archive documents. In: ICDAR, pp. 538–542. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  14. 14.
    Leedham, G., Yan, C., Takru, K., Tan, J.H.N., Mian, L.: Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In: International Conference on Document Analysis and Recognition, pp. 859–864 (2003)Google Scholar
  15. 15.
    Tan, C.L., Cao, R., Wang, Q., Shen, P.: Text extraction from historical handwritten documents by edge detection. In: ICARCV2000. 6th International Conference on Control, Automation, Robotics and Vision, Singapore (December 5-8, 2000)Google Scholar
  16. 16.
    Tan, Cao, Shen: Restoration of archival documents using a wavelet technique. IEEETPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002)Google Scholar
  17. 17.
    Barni, M., Bartolini, F., Cappellini, V.: Image processing for virtual restoration of artworks. IEEE MultiMedia 7(2), 34–37 (2000)CrossRefGoogle Scholar
  18. 18.
    de Rosa, A., Bonacchi, A.M., Cappellini, V., Barni, M.: Image segmentation and region filling for virtual restoration of artworks. In: International Conference on Image Processing, vol. 1, pp. 562–565 (2001)Google Scholar
  19. 19.
    Stanco, F., Ramponi, G., Tenze, L.: Removal of Semi-Transparent Blotches in Old Photographic Prints. In: Proc. 5th COST 276 Workshop, Prague, Czech Republic (2003)Google Scholar
  20. 20.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986)Google Scholar
  21. 21.
    Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognition 33(2), 225–236 (2000)CrossRefGoogle Scholar
  22. 22.
    Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  23. 23.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 8, 679–698 (1986)CrossRefGoogle Scholar
  24. 24.
    Pinto, J.R.C., Bandeira, L., Sousa, J.M.C., Pina, P.: Combining fuzzy clustering and morphological methods for old documents recovery. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, p. 387. Springer, Heidelberg (2005)Google Scholar
  25. 25.
    Bezdek, J.C. (ed.): Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, NY (1981)Google Scholar
  26. 26.
    Junker, M., Dengel, A., Hoch, R.: On the evaluation of document analysis components by recall, precision, and accuracy. In: ICDAR, pp. 713–716 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pedro Castro
    • 1
  • J. R. Caldas Pinto
    • 1
  1. 1.IDMEC/IST, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 LisboaPortugal

Personalised recommendations