For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.

https://link.springer.com/content/pdf/10.1007%2Fs13735-012-0004-6.pdf

Optical music recognition: state-of-the-art and open issues

This article describes the challenges posed by optical music recognition - a topic in com- puter science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.

The Challenge of Optical Music Recognition

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.

/pdf/a-comparative-study-of-staff-removal-algorithms-2hi1a227r4.pdf

A Comparative Study of Staff Removal Algorithms

The research literature concerning the automatic analysis of images of printed and handwritten music notation, for the period 1966 through 1990, is surveyed and critically examined.

A Critical Survey of Music Image Analysis

The preservation of musical works produced in the past requires their digitalization and transformation into a machine-readable format. The processing of handwritten musical scores by computers remains far from ideal. One of the fundamental stages to carry out this task is the staff line detection. We investigate a general-purpose, knowledge-free method for the automatic detection of music staff lines based on a stable path approach. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.

/pdf/staff-detection-with-stable-paths-21awdjdmdh.pdf

J. W. Roach

Papers

Using domain knowledge in low-level visual processing to interpret handwritten music: an experiment