Unsupervised Synthetic-to-Real Adaptation for Optical Music Recognition

The field of Optical Music Recognition (OMR) focuses on models capable of reading music scores from document images. Despite its growing popularity, OMR is still confined to settings where the target scores are similar in both musical context and visual presentation to the data used for training the model. The common scenario, therefore, involves manually annotating data for each specific case, a process that is not only labor-intensive but also raises concerns regarding practicality. We present a methodology based on training a neural model with synthetic images, thus reducing the difficulty of obtaining labeled data. As sheet music renderings depict regular visual characteristics compared to scores from real collections, we propose an unsupervised neural adaptation approach consisting of loss functions that promote alignment between the features learned by the model and those of the target collection while preventing the model from converging to undesirable solutions. This unsupervised adaptation bypasses the need for extensive retraining, requiring only the unlabeled target images. Our experiments, focused on music written in Mensural notation, demonstrate that the methodology is successful and that synthetic-to-real adaptation is indeed a promising way to create practical OMR systems with little human effort.