期刊
PATTERN RECOGNITION LETTERS
卷 123, 期 -, 页码 1-8出版社
ELSEVIER
DOI: 10.1016/j.patrec.2019.02.029
关键词
Optical music recognition; Handwritten music recognition; Document image analysis and recognition; Deep neural networks; LSTM
资金
- Spanish project [TIN2015-70924-C2-2-R]
- CERCA Program/Generalitat de Catalunya
- FPU fellowship [FPU15/06264]
- Ramon y Cajal Fellowship [RYC-2014-16831]
- FI fellowship (Secretaria d'Universitats i Recerca of the Generalitat de Catalunya) [AGAUR 2018 FI_B 00546]
- FI fellowship (Fons Social Europeu) [AGAUR 2018 FI_B 00546]
- NVIDIA Corporation
Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of hand-written music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. (C) 2019 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据