@InProceedings{ArnauBaro2022,
author="Arnau Baro
and Carles Badal
and Pau Torras
and Alicia Fornes",
title="Handwritten Historical Music Recognition through Sequence-to-Sequence with Attention Mechanism",
booktitle="3rd International Workshop on Reading Music Systems (WoRMS2021)",
year="2022",
pages="55--59",
optkeywords="Optical Music Recognition",
optkeywords="Digits",
optkeywords="Image Classification",
abstract="Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks.",
optnote="DAG; 600.121; 600.162; 602.230; 600.140",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3734), last updated on Tue, 25 Apr 2023 15:57:44 +0200",
opturl="https://arxiv.org/abs/2212.00378",
file=":http://refbase.cvc.uab.es/files/BBT2022.pdf:PDF"
}