Exploring federated learning in optical music recognition

Optical Music Recognition (OMR) technology plays a crucial role in the preservation of cultural heritage by automating the digitization of music documents, enabling their storage in symbolic formats and their subsequent analysis through digital tools. Progress in this field is, however, constrained by limited data availability: access to or distribution of music collections is frequently restricted due to legal or ownership barriers. This work investigates a potential mitigation of this issue through Federated Learning (FL) strategies, which enable decentralized training and eliminate the need to release restricted collections. Our methodology assumes a setting in which client nodes operate on small, heterogeneous corpora, while evaluation is conducted on established OMR benchmark collections. We examine widely used FL aggregation techniques, such as FedAvg, FedProx, and SCAFFOLD; as well as modern methodologies, such as FedKT. In addition, we introduce two modules specifically designed for OMR: FedClassPrior , which integrates class-prior information to improve symbol balance, and FedNGram , which supports decentralized language modeling to exploit notational regularities during decoding. Experimental results show consistent accuracy improvements when using FL compared to purely local training. Furthermore, the proposed modules substantially narrow the performance gap between standard FL and the ideal scenario in which centralized training is feasible. • First use of federated learning for end-to-end OMR. • Two specific OMR modules: FedClassPrior and FedNGram. • Experiments with fragmented client data and OMR benchmarks. • Federated models reach performance close to centralized training. • Proposed modules deliver additional accuracy improvements.