# Understanding Optical Music Recognition

## TL;DR

This paper provides a comprehensive overview of Optical Music Recognition (OMR), including its definition, relationship to related fields, taxonomy, and the impact of deep learning, aiming to make the field more accessible and structured.

## Contribution

It offers a clear definition of OMR, analyzes its process, introduces a novel taxonomy of applications, and discusses the influence of deep learning on modern OMR research.

## Key findings

- Provides a robust definition of OMR
- Introduces a new taxonomy of OMR applications
- Discusses the impact of deep learning on OMR research

## Abstract

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.