Publication at Faculty of Mathematics and Physics |
2022
Abstract
Training with synthetic data has been successfully used in many domains of deep learning where authentic training data is scarce. Optical Music Recognition (OMR), especially recognition of handwritten music, greatly benefits from training on synthetic data too.
In this paper, we explore the challenges of synthesizing images of sheets of music for training deep learning OMR models and compare such synthesis to the process of digital music engraving. We also contrast that with the architecture of our synthesizer prototype, which was used to achieve state-ofthe-art results by training on the synthetic images only.