MUSER: A prototype musical score recognition system using mathematical morphology

Abstract

Music representation utilizes a fairly rich repertoire of symbols. These symbols appear on a score sheet with relatively little shape distortion, differing from the prototype symbol shapes mainly by a positional translation and scale change. The prototype system we describe in this article is aimed at recognizing printed music notation from digitized music score images. The recognition system is composed of two parts: a low-level vision module that uses morphological algorithms for symbol detection and a high-level module that utilizes prior knowledge of music notation to reason about spatial positions and spatial sequences of these symbols. The high-level module also employs verification procedures to check the veracity of the output of the morphological symbol recognizer. The system produces an ASCII representation of music scores that can be input to a music-editing system. Mathematical morphology provides us the theory and the tools to analyze shapes. This characteristic of mathematical morphology lends itself well to analyzing and subsequently recognizing music scores that are rich in well-defined musical symbols. Since morphological operations can be efficiently implemented in machine vision systems that have special hardware support, the recognition task can be performed in near real-time. The system achieves accuracy in excess of 95% on the sample scores processed so far with a peak accuracy of 99.7% for the quarter and eighth notes, demonstrating the efficacy of morphological techniques for shape extraction.