Skip to main content
Log in

Optical recognition of music symbols

A comparative study

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

Abstract

Many musical works produced in the past are still currently available only as original manuscripts or as photocopies. The preservation of these works requires their digitalization and transformation into a machine-readable format. However, and despite the many research activities on optical music recognition (OMR), the results for handwritten musical scores are far from ideal. Each of the proposed methods lays the emphasis on different properties and therefore makes it difficult to evaluate the efficiency of a proposed method. We present in this article a comparative study of several recognition algorithms of music symbols. After a review of the most common procedures used in this context, their respective performances are compared using both real and synthetic scores. The database of scores was augmented with replicas of the existing patterns, transformed according to an elastic deformation technique. Such transformations aim to introduce invariances in the prediction with respect to the known variability in the symbols, particularly relevant on handwritten works. The following study and the adopted databases can constitute a reference scheme for any researcher who wants to confront a new OMR algorithm face to well-known ones.

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.

Similar content being viewed by others

References

  1. Arica N., Yarman-Vural F.: An overview of character recognition focused on off-line handwriting. IEEE Trans. Syst., Man, Cybern., Part C: Applica. Rev. 31(2), 216–233 (2001). doi:10.1109/5326.941845

    Article  Google Scholar 

  2. Bainbridge, D.: An extensible optical music recognition system. In: Nineteenth Australasian Computer Science Conference, pp. 308–317 (1997)

  3. Baird, H.: Document image defect models and their uses. pp. 62–67 (1993). doi:10.1109/ICDAR.1993.395781

  4. Bellini, P., Bruno, I., Nesi, P.: Optical music sheet segmentation. In: Proceedings of the 1st International Conference on Web Delivering of Music, pp. 183–190 (2001)

  5. Blostein D., Baird H.S.: A critical survey of music image analysis. In: Baird Bunke, Y. (eds) Structured Document Image Analysis, pp. 405–434. Springer, Heidelberg (1992)

    Google Scholar 

  6. Bojovic, M., Savic, M.D.: Training of hidden Markov models for cursive handwritten word recognition. In: ICPR ’00: Proceedings of the International Conference on Pattern Recognition, p. 1973. IEEE Computer Society, Washington, DC, USA (2000)

  7. Capela, A., Rebelo, A., Cardoso, J.S., Guedes, C.: Staff line detection and removal with stable paths. In: Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2008), pp. 263–270 (2008). http://www.inescporto.pt/~jsc/publications/conferences/2008ACapelaSIGMAP.pdf

  8. Cardoso, J.S., Capela, A., Rebelo, A., Guedes, C.: A connected path approach for staff detection on a music score. In: Proceedings of the International Conference on Image Processing (ICIP 2008), pp. 1005–1008 (2008)

  9. Cardoso J.S., Capela A., Rebelo A., Guedes C., da Costa J.P.: Staff detection with stable paths. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1134–1139 (2009). doi:10.1109/TPAMI.2009.34

    Article  Google Scholar 

  10. Coüasnon, B.: Segmentation et reconnaissance de documents guidées par la connaissance a priori: application aux partitions musicales. Ph.D. thesis, Université de Rennes (1996)

  11. Coüasnon, B., Camillerapp, J.: Using grammars to segment and recognize music scores. In: Proceedings of DAS-94: International Association for Pattern Recognition Workshop on Document Analysis Systems, pp. 15–27. Kaiserslautern (1993)

  12. Dalitz, C., Droettboom, M., Czerwinski, B., Fujigana, I.: Staff removal toolkit for gamera (2005–2007). http://music-staves.sourceforge.net

  13. Dalitz C., Droettboom M., Czerwinski B., Fujigana I.: A comparative study of staff removal algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 30, 753–766 (2008)

    Article  Google Scholar 

  14. Duda R.O., Hart P.E., Stork D.G.: Pattern Classification (2nd Edn.). Wiley, New York (2000)

    Google Scholar 

  15. Fornés, A., Lladós, J., Sánchez, G.: Primitive segmentation in old handwritten music scores. In: Liu, W., Lladós, J. (eds.) GREC, Lecture Notes in Computer Science, vol. 3926, pp. 279–290. Springer (2005). http://dblp.uni-trier.de/db/conf/grec/grec2005.html#FornesLS05

  16. Fujinaga I.: Staff detection and removal. In: George, S. (eds) Visual Perception of Music Notation: On-Line and Off-Line Recognition, pp. 1–39. Idea Group Inc, Hershey (2004)

    Google Scholar 

  17. Gonzalez R.C., Woods R.E., Eddins S.L.: In: Digital Image processing using MATLAB, Pearson/Prentice-Hall, Upper Saddle River (2004)

  18. Haykin, S.: Neural Networks: A Comprehensive Foundation (2nd edn.). Prentice Hall (1998). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0132733501

  19. Jain A.K., Zhong Y., Lakshmanan S.: Object matching using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell. 18(3), 267–278 (1996). doi:10.1109/34.485555

    Article  Google Scholar 

  20. Jain A.K., Zongker D.: Representation and recognition of handwritten digits using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1386–1391 (1997). doi:10.1109/34.643899

    Article  Google Scholar 

  21. Kanungo, T.: Document degradation models and a methodology for degradation model validation. Ph.D. thesis, Seattle, WA, USA (1996)

  22. Kanungo T., Haralick R., Baird H., Stuezle W., Madigan D.: A statistical, nonparametric methodology for document degradation model validation. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1209–1223 (2000). doi:10.1109/34.888707

    Article  Google Scholar 

  23. Kopec, G.E., Parc, P.A.C., Maltzcarnegie, D.A.: Markov source model for printed music decoding. J Electron Imaging, pp. 7–14 (1996)

  24. Lam L., Suen C.Y.: Structural classification and relaxation matching of totally unconstrained handwritten zip-code numbers. Pattern Recognit. 21(1), 19–32 (1988). doi:10.1016/0031-3203(88)90068-4

    Article  Google Scholar 

  25. Mitobe, Y., Miyao, H., Maruyama, M.: A fast HMM algorithm based on stroke lengths for on-line recognition of handwritten music scores. In: IWFHR ’04: Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 521–526. IEEE Computer Society, Washington (2004). doi:10.1109/IWFHR.2004.2

  26. Miyao H., Nakano Y.: Note symbol extraction for printed piano scores using neural networks. IEICE Trans. Inf. Syst. E79–D, 548–554 (1996)

    Google Scholar 

  27. Miyao H., Okamoto M.: Stave extraction for printed music scores using DP matching. J Adv. Comput. Intell. Intell. Inform. 8, 208–215 (2007)

    Google Scholar 

  28. Ng K.: Optical music analysis for printed music score and handwritten music manuscript. In: George, S. (eds) Visual Perception of Music Notation: On-Line and Off-Line Recognition, pp. 108–127. Idea Group Inc, Hershey (2004)

    Google Scholar 

  29. Nishida H.: A structural model of shape deformation. Pattern Recognit. 28(10), 1611–1620 (1995)

    Article  Google Scholar 

  30. Pugin, L.: Optical music recognition of early typographic prints using hidden Markov models. In: ISMIR, pp. 53–56 (2006)

  31. Randriamahefa, R., Cocquerez, J., Fluhr, C., Pepin, F., Philipp, S.: Printed music recognition. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 898–901 (1993). doi:10.1109/ICDAR.1993.395592

  32. Reed K.T., Parker J.R.: Automatic computer recognition of printed music. Proc. 13th Int. Conf. Pattern Recognit. 3, 803–807 (1996). doi:10.1109/ICPR.1996.547279

    Article  Google Scholar 

  33. Rossant F., Bloch I.: Robust and adaptive omr system including fuzzy modeling, fusion of musical rules, and possible error detection. EURASIP J. Adv. Signal Process. 2007(1), 160–160 (2007). doi:10.1155/2007/81541

    Google Scholar 

  34. Szwoch M.: A robust detector for distorted music staves. In: Computer Analysis of Images and Patterns, pp. 701–708. Springer, Heidelberg (2005)

    Google Scholar 

  35. Toyama, F., Shoji, K., Miyamichi, J.: Symbol recognition of printed piano scores with touching symbols. pp. 480–483 (2006). doi:10.1109/ICPR.2006.1099

  36. Vapnik, V.N.: Statistical Learning Theory. Wiley (1998). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0471030031

  37. Wakahara T.: Shape matching using LAT and its application to handwritten numeral recognition. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 618–629 (1994). doi:10.1109/34.295906

    Article  Google Scholar 

  38. Wang, Y.K., Fan, K.C., Juang, Y.T., Chen, T.H.: Using hidden Markov model for chinese business card recognition. In: ICIP (1), pp. 1106–1109 (2001)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Rebelo.

Additional information

This work was partially funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal through project PTDC/EIA/71225/2006.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rebelo, A., Capela, G. & Cardoso, J.S. Optical recognition of music symbols. IJDAR 13, 19–31 (2010). https://doi.org/10.1007/s10032-009-0100-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-009-0100-1

Keywords

Navigation