Alicia Fornes, & Gemma Sanchez. (2014). Analysis and Recognition of Music Scores. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. E, pp. 749–774). Springer London.
Abstract: The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented.
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Alicia Fornes, Josep Llados, & Gemma Sanchez. (2006). Primitive Segmentation in Old Handwritten Music Scores. In Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299.
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Joan Mas, J.A. Jorge, Gemma Sanchez, & Josep Llados. (2008). Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities, (Vol. 5046, 176–187). LNCS.
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Ernest Valveny, Philippe Dosch, & Alicia Fornes. (2008). Report on the Third Contest on Symbol Recognition. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 321–328). LNCS.
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Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Herve Locteau, & Eric Trupin. (2008). Building Synthetic Graphical Documents for Performance Evaluation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 288–298). LNCS.
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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez, & Joan Mas. (2008). Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 30–40). LNCS.
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Alicia Fornes, Josep Llados, & Gemma Sanchez. (2008). Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 52–60). LNCS.
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Jose Antonio Rodriguez, & Florent Perronnin. (2008). Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 188–198). LNCS.
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Jose Antonio Rodriguez, Gemma Sanchez, & Josep Llados. (2008). Categorization of Digital Ink Elements using Spectral Features. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 188–198). LNCS. Springer–Verlag.
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