# Deep Neural Networks for Document Processing of Music Score Images
## 1 Introduction  
Significant efforts for the preservation of music heritage have occurred in recent decades. The digitization process has significantly improved the access to these sources while ensuring their physical preservation; however, to make the music contained in these documents truly browsable and searchable, it is necessary to encode the symbolic information into a structured digital format such as MusicXML or Music Encoding Initiative (MEI). The full process of digitization and encoding not only allows the preservation of that cultural heritage, but also provides means to perform other interesting tasks such as search and retrieval by content, conversion between different musical notations, or large-scale computational music analysis. A digitized music document can be manually converted into a structured digital format, but this approach has two big disadvantages. On the one hand, users must label every single symbol in the document, leading to a tedious and hardly scalable process. On the other hand, the transcription should ideally be conducted by experts in the particular notational style of the piece, and so the whole process becomes expensive as well. Furthermore, manual transcription tends to be prone to human error due to fatigue, hence needing the use of precautionary measures such as double entry. The research field known as Optical Music Recognition (OMR) focuses on the automatic detection and encoding of musical content from scanned images [1]. Once musical documents have been digitized, and their information encoded in a structured format, aforementioned computational studies can be carried out with the symbolic corpora. Nevertheless, the usual bottleneck of this process is the automatic transcription by OMR systems, whose current performance is far from optimal [2]. The main problems of OMR are three-fold: (i) music notation is complex to analyze, compared with other documents such as text; (ii) music symbols are usually connected and overlaid, which makes them hard to isolate and classify; and (iii) music documents, especially from the Medieval and Renaissance era, come in a wide variety of notational styles and formats, resulting in a heterogeneous collection. In comparison to other similar domains such as text, the processing of music score images can be quite difficult given the complexity of music notation and the wealth of information contained in these documents. In addition to the musical notes that are usually placed over the staff lines, music scores may also contain several types of information such as alterations, lyrics, decorations, or bibliographic information about the piece. Therefore, before any attempt of automatic recognition of the content, it is useful to perform a document processing to separate and classify the different zones of the image into their corresponding categories. Note that this categorization must be performed in the first stages of the process and, quite often, represents a difficult barrier to overcome before moving on to the remaining stages. Thus far, OMR researchers have mainly developed heuristic strategies and systems for document processing that deal only with the specific type of scores at hand, as can be seen in more detail in Section 2, exploiting specific details of the images to improve the performance of the recognition. This approach seems beneficial in the short term but it is not flexible or generalizable. In many cases, a new OMR system is developed anew for dealing with different manuscripts (e.g., with a different notation, from a different time period, or with a different level of image degradation), resulting in a slow progress in the field. In this article, we discuss a general framework for the document processing of music scores based on pure machine learning that can be applied to any music notation style or musical document.