Human Vs. Machine: Comparing Selection Strategies in Active Learning for Optical Music Recognition

Optical Music Recognition (OMR) systems rely on accurate layout analysis (LA) to segment different information layers in music score images. While deep learning approaches have improved performance, they remain heavily dependent on large amounts of annotated data. In this work, we propose the integration of a Few-Shot Learning (FSL) architecture into an active learning framework for LA. This enables interactive and iterative training, allowing the model to progressively improve from minimal annotated data. We evaluate how this approach enhances recognition accuracy and reduces annotation effort, and we study the impact of different sample selection criteria within this framework, comparing data selected by five expert annotators against four automated strategies: random, sequential, ink density-based, and entropy-based. Experiments across three diverse music score datasets show that entropy-based selection consistently outperforms human choices, achieving an F1-score of 81.1% with only 8 labeled patches, while humans required at least 16 to reach similar performance. Our method improves over existing FSL approaches by up to 21.6% and substantially reduces annotation time. These results suggest that automated strategies can offer more efficient alternatives to human selection in OMR annotation workflows.