An Optical Music Recognition Tool for String Quartets

Abstract

This paper presents an optical music recognition (OMR) system tailored for PDF scores of string quartet pieces. The approach begins with a SegNet-based segmentation model to localize musical regions, followed by convolutional neural networks and image processing techniques to classify and extract musical symbols. To improve rhythmic precision, dynamic programming methods enforce time signature constraints and explicitly handle tuplets. Evaluation using a longest common subsequence metric across multiple feature matrices demonstrates robust accuracy in recognizing pitch, onset, duration, and accidentals on a benchmark dataset. Code and data are available at https://github.com/lattellie/25-omr.

arXiv comment3 pages, 1 figure, 1 table. Extended abstract for the Late-Breaking/Demo Session of ISMIR 2025, Daejeon, South Korea