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Writer Identification in Old Handwritten Music Scores

Writer Identification in Old Handwritten Music Scores

Alicia Fornés, Josep Lladós, Gemma Sánchez, Horst Bunke
Copyright: © 2012 |Pages: 37
ISBN13: 9781609607869|ISBN10: 1609607864|EISBN13: 9781609607876
DOI: 10.4018/978-1-60960-786-9.ch002
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MLA

Fornés, Alicia, et al. "Writer Identification in Old Handwritten Music Scores." Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology, edited by Constantin Papaodysseus, IGI Global, 2012, pp. 27-63. https://doi.org/10.4018/978-1-60960-786-9.ch002

APA

Fornés, A., Lladós, J., Sánchez, G., & Bunke, H. (2012). Writer Identification in Old Handwritten Music Scores. In C. Papaodysseus (Ed.), Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology (pp. 27-63). IGI Global. https://doi.org/10.4018/978-1-60960-786-9.ch002

Chicago

Fornés, Alicia, et al. "Writer Identification in Old Handwritten Music Scores." In Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology, edited by Constantin Papaodysseus, 27-63. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-786-9.ch002

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Abstract

Writer identification in handwritten text documents is an active area of study, whereas the identification of the writer of graphical documents is still a challenge. The main objective of this work is the identification of the writer in old music scores, as an example of graphic documents. The writer identification framework proposed combines three different writer identification approaches. The first one is based on the use of two symbol recognition methods, robust in front of hand-drawn distortions. The second one generates music lines and extracts information about the slant, width of the writing, connected components, contours and fractals. The third approach generates music texture images and computes textural features. The high identification rates obtained demonstrate the suitability of the proposed ensemble architecture. To the best of our knowledge, this work is the first contribution on writer identification from images containing graphical languages.

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