# End-to-End Neural Optical Music Recognition of Monophonic Scores
## 1 Introduction  
During the past few years, the availability of huge collections of digital scores has facilitated both the music professional practice and the amateur access to printed sources that were difficult to obtain in the past. Some examples of these collections are the IMSLP ([[http://imslp.org](http://imslp.org)]([http://imslp.org](http://imslp.org))) website with currently 425,000 classical music scores, or many different sites offering Real Book jazz lead sheets. Furthermore, many efforts are being done by private and public libraries to publish their collections online (see [[http://drm.ccarh.org/](http://drm.ccarh.org/)]([http://drm.ccarh.org/](http://drm.ccarh.org/))). However, in addition to this instant availability, the advantages of having the digitized image of a work over its printed material are restricted to the ease to copy and distribute, and the lack of wear that digital media intrinsically offers over any physical resource. The great possibilities that current music-based applications can offer are restricted to scores symbolically encoded. Notation software such as Finale ([[https://www.finalemusic.com](https://www.finalemusic.com)]([https://www.finalemusic.com](https://www.finalemusic.com))), Sibelius ([[https://www.avid.com/en/sibelius](https://www.avid.com/en/sibelius)]([https://www.avid.com/en/sibelius](https://www.avid.com/en/sibelius))), MuseScore ([[https://musescore.org](https://musescore.org)]([https://musescore.org](https://musescore.org))), or Dorico ([[https://www.steinberg.net/en/products/dorico](https://www.steinberg.net/en/products/dorico)]([https://www.steinberg.net/en/products/dorico](https://www.steinberg.net/en/products/dorico))), computer-assisted composition applications such as OpenMusic ([[http://repmus.ircam.fr/openmusic/](http://repmus.ircam.fr/openmusic/)]([http://repmus.ircam.fr/openmusic/](http://repmus.ircam.fr/openmusic/))), digital musicology systems such as Music21 ([[http://web.mit.edu/music21/](http://web.mit.edu/music21/)]([http://web.mit.edu/music21/](http://web.mit.edu/music21/))), or Hundrum ([[http://www.humdrum.org/](http://www.humdrum.org/)]([http://www.humdrum.org/](http://www.humdrum.org/))), or content-based search tools [1], cannot deal with pixels contained in digitized images but with computationally-encoded symbols such as notes, bar-lines or key signatures. Furthermore, the scientific musicological domain would dramatically benefit from the availability of digitally encoded music in symbolic formats such as MEI [2] or MusicXML [3]. Just to name an example, many of the systems presented in the Computational Music Analysis book edited by [PERSON] [4] cannot be scaled to real-world scenarios due to the lack of big enough symbolic music datasets. Many different initiatives have been proposed to manually fill this gap between digitized music images and digitally encoded music content such as OpenScore ([[https://openscore.cc](https://openscore.cc)]([https://openscore.cc](https://openscore.cc))), KernScores ([[http://kern.ccarh.org](http://kern.ccarh.org)]([http://kern.ccarh.org](http://kern.ccarh.org))), or RISM [5]--encoding just small excerpts (incipits). However, the manual transcription of music scores does not represent a scalable process, given that its cost is prohibitive both in time and resources. Therefore, to face this scenario with guarantees, it is necessary to resort to assisted or automatic transcription systems. The so-called Optical Music Recognition (OMR) is defined as the research about teaching computers how to read musical notation [6], with the ultimate goal of exporting their content to a desired format. Despite the great advantages of its development, OMR is far from being totally reliable as a black box, as current optical character recognition [7] or speech recognition [8] technologies do. Commercial software is constantly being improved by fixing specific problems from version to version. In the scientific community, there are hardly any complete approach for its solution [9; 10].