A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenarios

This work presents a novel region-based layout analysis (LA) method for Optical Music Recognition (OMR) systems, aimed at overcoming the data scarcity challenge. Contemporary OMR techniques, grounded in machine learning principles, have a critical requirement: a labeled dataset for training. This presents a practical challenge due to the extensive manual effort required, coupled with the fact that the availability of suitable data for creating training sets is not always guaranteed. Unlike other approaches, our method focuses on adapting the training and sample extraction processes within an existing neural network framework. Our approach incorporates a labeled data-driven oversampling technique, a masking layer to enable training with partial labeling, and an adaptive scaling process to improve results for varying score sizes. Through comprehensive experimentation, we established the minimal labeled data necessary for an effective model and demonstrated that our method could achieve a performance comparable with the state-of-the-art with just 8 to 32 labeled samples. The implications of our research extend beyond improving LA, providing a scalable and practical solution for digitizing and preserving musical documents.