Optical Music Recognition in Manuscripts from the Ricordi Archive

TL;DR

This paper presents a neural network-based approach to automatically recognize and categorize musical elements in digitized manuscripts from the Ricordi archive, facilitating digital musicology research.

Contribution

It introduces a methodology for training classifiers on annotated manuscript images to distinguish musical symbols, with publicly available data and models for reproducibility.

Findings

High accuracy in classifying musical elements

Effective differentiation between noise and actual music symbols

Framework for automatic music manuscript analysis

Abstract

The Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. This process has allowed us to automatically extract samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer's annotations, among others. To distinguish between digitization noise and actual music elements, a subset of these images was meticulously grouped and labeled by multiple individuals into several classes. After assessing the consistency of the annotations, we trained multiple neural network-based classifiers to differentiate between the identified music elements. The primary objective of this study was to evaluate the reliability of these classifiers, with the ultimate goal of using them for the automatic categorization of the remaining unannotated data set. The dataset, complemented by manual annotations, models, and source code used in these experiments are publicly accessible for replication purposes.