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Alicia Fornes and Josep Llados. 2010. A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores. 12th International Conference on Frontiers in Handwriting Recognition.634–639.
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we introduce a symbol-dependent approach for identifying the writer of old music scores, which is based on two symbol recognition methods. The main idea is to use the Blurred Shape Model descriptor and a DTW-based method for detecting, recognizing and describing the music clefs and notes. The proposed approach has been evaluated in a database of old music scores, achieving very high writer identification rates.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2005. Primitive Segmentation in Old Handwritten Music Scores.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2005. Staff and graphical primitive segmentation in old handwritten music scores.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2006. Primitive Segmentation in Old Handwritten Music Scores. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2007. Old Handwritten Musical Symbol Classification by a Dynamic Time Warping Based Method. Seventh IAPR International Workshop on Graphics Recognition.26–27.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2008. Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.52–60. (LNCS.)
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Alicia Fornes, Josep Llados, Gemma Sanchez and Dimosthenis Karatzas. 2010. Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model. IJDAR, 13(3), 229–241.
Abstract: One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2008. Writer Identification in Old Handwritten Music Scores. Proceedings of the 8th International Workshop on Document Analysis Systems,.347–353.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2009. Symbol-independent writer identification in old handwritten music scores. In proceedings of 8th IAPR International Workshop on Graphics Recognition. Springer Berlin Heidelberg, 186–197.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2009. On the use of textural features for writer identification in old handwritten music scores. 10th International Conference on Document Analysis and Recognition.996–1000.
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.
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