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.
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References
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
Bainbridge, D.: An extensible optical music recognition system. In: Nineteenth Australasian Computer Science Conference, pp. 308–317 (1997)
Baird, H.: Document image defect models and their uses. pp. 62–67 (1993). doi:10.1109/ICDAR.1993.395781
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)
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)
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)
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
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)
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
Coüasnon, B.: Segmentation et reconnaissance de documents guidées par la connaissance a priori: application aux partitions musicales. Ph.D. thesis, Université de Rennes (1996)
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)
Dalitz, C., Droettboom, M., Czerwinski, B., Fujigana, I.: Staff removal toolkit for gamera (2005–2007). http://music-staves.sourceforge.net
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)
Duda R.O., Hart P.E., Stork D.G.: Pattern Classification (2nd Edn.). Wiley, New York (2000)
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
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)
Gonzalez R.C., Woods R.E., Eddins S.L.: In: Digital Image processing using MATLAB, Pearson/Prentice-Hall, Upper Saddle River (2004)
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
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
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
Kanungo, T.: Document degradation models and a methodology for degradation model validation. Ph.D. thesis, Seattle, WA, USA (1996)
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
Kopec, G.E., Parc, P.A.C., Maltzcarnegie, D.A.: Markov source model for printed music decoding. J Electron Imaging, pp. 7–14 (1996)
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
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
Miyao H., Nakano Y.: Note symbol extraction for printed piano scores using neural networks. IEICE Trans. Inf. Syst. E79–D, 548–554 (1996)
Miyao H., Okamoto M.: Stave extraction for printed music scores using DP matching. J Adv. Comput. Intell. Intell. Inform. 8, 208–215 (2007)
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)
Nishida H.: A structural model of shape deformation. Pattern Recognit. 28(10), 1611–1620 (1995)
Pugin, L.: Optical music recognition of early typographic prints using hidden Markov models. In: ISMIR, pp. 53–56 (2006)
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
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
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
Szwoch M.: A robust detector for distorted music staves. In: Computer Analysis of Images and Patterns, pp. 701–708. Springer, Heidelberg (2005)
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
Vapnik, V.N.: Statistical Learning Theory. Wiley (1998). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0471030031
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
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)
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This work was partially funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal through project PTDC/EIA/71225/2006.
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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
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DOI: https://doi.org/10.1007/s10032-009-0100-1