Optical Music Recognition and Human-in-the-loop Computation

Liang Chen, Christopher Raphael

Abstract: We present our work in optical music recognition in which we seek to transform scanned music notation images into symbolic representations. 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-interactive approach. We define a simple communication channel between the user and recognition engine, in which the user imposes pixel-level or model-level constraints, to improve our automatic OMR system.

Keywords: Optical Music Recognition, Human-in-the-loop Computation

TL;DR: An OMR system that involves human interaction and uses human feedback to improve the recognition.