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Handwritten Music Object Detection: Open Issues and Baseline Results

Abstract : Optical Music Recognition (OMR) is the challenge of understanding the content of musical scores. Accurate detection of individual music objects is a critical step in processing musical documents, because a failure at this stage corrupts any further processing. So far, all proposed methods were either limited to typeset music scores or were built to detect only a subset of the available classes of music symbols. In this work, we propose an end-to-end trainable object detector for music symbols that is capable of detecting almost the full vocabulary of modern music notation in handwritten music scores. By training deep convolutional neural networks on the recently released MUSCIMA++ dataset which has symbol-level annotations, we show that a machine learning approach can be used to accurately detect music objects with a mean average precision of up to 80%.
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Submitted on : Monday, January 7, 2019 - 4:47:35 PM
Last modification on : Wednesday, November 3, 2021 - 6:05:35 AM
Long-term archiving on: : Monday, April 8, 2019 - 4:17:35 PM


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  • HAL Id : hal-01972424, version 1


Alexander Pacha, Kwon-Young Choi, Bertrand B. Coüasnon, Yann Ricquebourg, Richard Zanibbi, et al.. Handwritten Music Object Detection: Open Issues and Baseline Results. 13th IAPR International Workshop on Document Analysis Systems, Apr 2018, Vienne, Austria. ⟨hal-01972424⟩



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