Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, which we refer to as Optical Music Recognition. 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…

Active Researchers

To the best of our knowledge, the following people are actively researching and working on OMR (in alphabetical order): Baro, Arnau Byrd, Donald Calvo-Zaragoza, Jorge Castellanos, Francisco J. Coüasnon, Bertrand Choi, Kwon-Young Crawford, Tim Elezi, Ismail Fornés, Alicia Fujinaga, Ichiro Pacha, Alexander Ringwalt, Dan Rizo, David Roggenkemper, Heinz Tuggener, Lukas Vigliensoni, Gabriel Please contact us…

Getting started with OMR

During ISMIR 2018, Ichiro Fujinaga, Jorge Calvo-Zaragoza, Jan Hajič jr., and Alexander Pacha gave a tutorial that was recorded and is available on our YouTube Channel. Icons on this website made by Freepik from http://www.flaticon.com are licensed by CC 3.0 BY.

WoRMS 2021 Videos Online

In case you missed this year’s edition of WoRMS 2021 in Alicante, there are good news for you. All presentations have been recorded in Zoom and available on YouTube in our Optical Music Recognition Channel.

Assessing PlayScore

End of August the German computer magazine c’t published a thorough review of programs that digitize sheet music (18/2019, page 122-126). The three programs that achieved ‘good recognition’ were PhotoScore, SharpEye, and SmartScore with a reported 98% accuracy – the same programs that did well in the review that c’t published in 2001! PlayScore was…

Introduction to MUSCIMA++

In this video, Jan Hajič jr. introduces the MUSCIMA++ dataset and explains how it can be used to solve OMR.

Assessments for slurs/ties and for articulations

When I wrote the first blog post about assessments about a month ago, I noted that OMR products had trouble with slurs and articulations. So an assessment of both seems very useful! Let us have a look at the following stave: One of the OMR outputs looked like this: The assessment is fairly straight-forward: a slur…

Assessments without MusicXML

In the last weeks, I have shown how a note/rest assessment works for OMR systems that supports MusicXML as an output. There are of course OMR systems in development that do not support MusicXML and may not be able to process a full page. How can the note/rest assessment handle such situations? To address this,…

Short update to automated assessments

Evaluating scores on the note level is the first step towards automated assessments. In this blog post, I’ll take a look at how this can be done.

Can automated assessments support OMR evaluations?

At the end of the blog I wrote about OMR competitions a couple of months ago, I mentioned that a prerequisite is automated evaluations. Last two months I familiarized myself with the topic using the excellent paper ‘Towards a Standard Testbed for Optical Music Recognition’ by Donald Byrd and Jakob Grue Simonsen, and the OMRTestSuite…