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Gemma Sanchez; Josep Llados; K. Tombre |
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A mean string algorithm to compute the average among a set of 2D shapes |
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2002 |
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Pattern Recognition Letters |
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PRL |
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23 |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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Ernest Valveny; Philippe Dosch |
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A general framework for the evaluation of symbol recognition methods |
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2006 |
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International Journal on Document Analysis and Recognition |
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DAG @ dag @ VaD2006 |
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686 |
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Ernest Valveny; Philippe Dosch |
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A General Framework for the Evaluation of Symbol Recognition Methods |
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2007 |
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International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74 |
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DAG @ dag @ VaD2007 |
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893 |
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Ernest Valveny; Philippe Dosch; Adam Winstanley; Yu Zhou; Su Yang; Luo Yan; Liu Wenyin; Dave Elliman; Mathieu Nicolas Delalandre; Eric Trupin; Sebastien Adam; Jean-Marc Ogier |
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A general framework for the evaluation of symbol recognition methods |
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2006 |
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International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74 |
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DAG @ dag @ VDW2006 |
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801 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke |
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Title |
A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores |
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2010 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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13 |
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4 |
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243-259 |
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The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers. |
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Springer-Verlag |
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1433-2833 |
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DAG; CAT;CIC |
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FLS2010b |
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1319 |
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