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arXiv · 2506.10488

Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation

Juan C. Martinez-Sevilla·Joan Cerveto-Serrano·Noelia Luna·Greg Chapman·Craig Sapp·David Rizo·Jorge Calvo-Zaragoza
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Abstract

In this work, we introduce the Sheet Music Benchmark (SMB), a dataset of six hundred and eighty-five pages specifically designed to benchmark Optical Music Recognition (OMR) research. SMB encompasses a diverse array of musical textures, including monophony, pianoform, quartet, and others, all encoded in Common Western Modern Notation using the Humdrum **kern format. Alongside SMB, we introduce the OMR Normalized Edit Distance (OMR-NED), a new metric tailored explicitly for evaluating OMR performance. OMR-NED builds upon the widely-used Symbol Error Rate (SER), offering a fine-grained and detailed error analysis that covers individual musical elements such as note heads, beams, pitches, accidentals, and other critical notation features. The resulting numeric score provided by OMR-NED facilitates clear comparisons, enabling researchers and end-users alike to identify optimal OMR approaches. Our work thus addresses a long-standing gap in OMR evaluation, and we support our contributions with baseline experiments using standardized SMB dataset splits for training and assessing state-of-the-art methods.

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Juan C. Martinez-Sevilla, Joan Cerveto-Serrano, Noelia Luna, Greg Chapman, Craig Sapp, David Rizo, Jorge Calvo-Zaragoza. 2025-06-12. Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation. https://doi.org/10.5281/zenodo.17811446

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