Discussion Group Summary: Optical Music Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11009)


This document summarizes the discussion of the interest group on Optical Music Recognition (OMR) that took place in the 12th IAPR International Workshop on Graphics Recognition, and presents the main conclusions drawn during the session: OMR should revisit how it describes itself, and the OMR community should intensify its collaboration both internally and with other stakeholders.


Optical Music Recognition Discussion group 



Jorge Calvo-Zaragoza acknowledges the support from the Spanish Ministerio de Economía, Industria y Competitividad through Juan de la Cierva - Formación grant (Ref. FJCI-2016-27873). Jan Hajič jr. acknowledges the support by the Czech Science Foundation grant no. P103/12/G084, Charles University Grant Agency grants 1444217 and 170217, and by SVV project 260 453.


  1. 1.
    Bainbridge, D., Bell, T.: A music notation construction engine for optical music recognition. Softw. Pract. Exp. 33(2), 173–200 (2003)CrossRefGoogle Scholar
  2. 2.
    Bellini, P., Bruno, I., Nesi, P.: Assessing optical music recognition tools. Comput. Music J. 31(1), 68–93 (2007)CrossRefGoogle Scholar
  3. 3.
    Byrd, D., Simonsen, J.G.: Towards a standard testbed for optical music recognition: definitions, metrics, and page images. J. New Music Res. 44(3), 169–195 (2014)CrossRefGoogle Scholar
  4. 4.
    Calvo-Zaragoza, J., Oncina, J.: Recognition of pen-based music notation: the HOMUS dataset. In: 22nd International Conference on Pattern Recognition, pp. 3038–3043 (2014)Google Scholar
  5. 5.
    Calvo-Zaragoza, J., Rizo, D.: End-to-end neural optical music recognition of monophonic scores. Appl. Sci. 8(4), 606–629 (2018)CrossRefGoogle Scholar
  6. 6.
    Dalitz, C., Droettboom, M., Pranzas, B., Fujinaga, I.: A comparative study of staff removal algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 753–766 (2008)CrossRefGoogle Scholar
  7. 7.
    Droettboom, M., Fujinaga, I.: Symbol-level groundtruthing environment for OMR. In: Proceedings of the 5th International Conference on Music Information Retrieval, pp. 497–500 (2004)Google Scholar
  8. 8.
    Fornés, A., Dutta, A., Gordo, A., Lladós, J.: CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal. Int. J. Doc. Anal. Recogn. 15(3), 243–251 (2012)CrossRefGoogle Scholar
  9. 9.
    Fujinaga, I.: Exemplar-based learning in adaptive optical music recognition system. In: International Computer Music Conference, pp. 55–56 (1996)Google Scholar
  10. 10.
    Hajič Jr., J., Pecina, P.: Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression. Computing Research Repository abs/1708.01806 (2017)Google Scholar
  11. 11.
    Hajič Jr., J., Pecina, P.: Groundtruthing (not only) music notation with musicmarker: a practical overview. In: 12th International Workshop on Graphics Recognition, pp. 47–48 (2017)Google Scholar
  12. 12.
    Hajič Jr., J., Pecina, P.: The MUSCIMA++ dataset for handwritten optical music recognition. In: 14th IAPR International Conference on Document Analysis and Recognition, pp. 39–46 (2017)Google Scholar
  13. 13.
    Hajič Jr., J., Novotný, J., Pecina, P., Pokorný, J.: Further steps towards a standard testbed for optical music recognition. In: Proceedings of the 17th International Society for Music Information Retrieval Conference, pp. 157–163. New York University (2016)Google Scholar
  14. 14.
    MacMillan, K., Droettboom, M., Fujinaga, I.: Gamera: optical music recognition in a new shell. In: Proceedings of the 2002 International Computer Music Conference (2002)Google Scholar
  15. 15.
    Miyao, H., Haralick, R.M.: Format of ground truth data used in the evaluation of the results of an optical music recognition system. In: IAPR workshop on document analysis systems, pp. 497–506 (2000)Google Scholar
  16. 16.
    Novotnỳ, J., Pokornỳ, J.: Introduction to optical music recognition: Overview and practical challenges. In: Necasky M., Moravec P., Pokorný, J. (eds.) Proceedings of the Dateso 2015 Annual International Workshop on DAtabases, TExts, Specifications and Objects, vol. 1343, pp. 65–76. CEUR-WS (2015)Google Scholar
  17. 17.
    Pacha, A., Choi, K.Y., Coüasnon, B., Ricquebourg, Y., Zanibbi, R., Eidenberger, H.: Handwritten music object detection: open issues and baseline results. In: 2018 13th IAPR Workshop on Document Analysis Systems (2018)Google Scholar
  18. 18.
    Padilla, V., Marsden, A., McLean, A., Ng, K.: Improving OMR for digital music libraries with multiple recognisers and multiple sources. In: 1st International Workshop on Digital Libraries for Musicology, pp. 1–8 (2014)Google Scholar
  19. 19.
    Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimedia Inf. Retrieval 1(3), 173–190 (2012)CrossRefGoogle Scholar
  20. 20.
    Saleh, Z., Zhang, K., Calvo-Zaragoza, J., Vigliensoni, G., Fujinaga, I.: Pixel.js: Web-based pixel classification correction platform for ground truth creation. In: 12th International Workshop on Graphics Recognition, pp. 39–40 (2017)Google Scholar
  21. 21.
    van der Wel, E., Ullrich, K.: Optical music recognition with convolutional sequence-to-sequence models. In: Proceedings of the 18th International Society for Music Information Retrieval Conference, pp. 731–737 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.PRHLT Research CenterUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Institute of Formal and Applied LinguisticsCharles UniversityPragueCzech Republic
  3. 3.Institute of Visual Computing and Human-Centered TechnologyViennaAustria

Personalised recommendations