Human-Guided Recognition of Music Score Images

Abstract

We present our ongoing work in optical music recognition in which we seek to transform printed music notation images into symbolic representations, suitable for playback, analysis, and rendering. While music notation contains a small core of symbols and primitives composed in a rule-bound way, there are a great many common exceptions to these rules, as well as a heavy tail of rarer symbols. Since our goal is to create symbolic representations with accuracy near that of published music scores, we doubt the feasibility of fully-automatic recognition, opting instead for a human-guided approach. We define a simple communication channel between the user and recognition engine, in which the user imposes pixel-level or model-level constraints.