# Deep Neural Networks for Document Processing of Music Score Images
## 2 Background  
The traditional workflow for OMR comprises several stages [9], the first of which is the document analysis of the music score image (see Figure 1), followed by symbol classification, in which each meaningful symbol of the image is assigned a category. Music notation itself is reconstructed in the next stage, after which the music is encoded into the desired symbolic music format. The approach presented here only deals with the document processing stage, a sub-stage of the general document analysis. We define the document processing as the detection and categorization of the different layers of information contained in the music score image. This step is key for providing robustness to the system. That is, if subsequent stages can assume the correct separation of the different layers, systems tend to generalize more easily. Given its importance, many researchers have proposed different algorithms to deal with specific steps within the document processing stage of the OMR workflow. A common first step of document processing is binarization. This process consists in separating the background (i.e., the superfluous part of the image) from the foreground (i.e., the relevant content), and is usually considered the starting point for the subsequent steps. Binarization is a critical step that is also relevant in other document-processing domains [10; 11; 12]. Therefore, a number of binarization techniques have been developed for general documents that can be applied for music documents as well [13; 14; 15]. Additionally, some music-specific document binarization techniques that take advantage of prior knowledge about music scores have been proposed. For example, BLIST method [16] consists of an adaptive local thresholding algorithm based on the estimation of features of staff lines in the score. In the work of [PERSON] et al. [17], a Markov Random Field is used to binarize the documents from a foreground modeling based on the color of the staff lines. However, the main problem found in these options is the varying performance depending on the characteristics of the document [18; 19]. A specific feature that OMR has to deal with is the presence of the staff. Although these lines are necessary for human readability and music interpretation, most OMR workflows are based on detecting and removing the staff lines before classifying the remaining elements in the score. Staff-line removal has been an active research field for many years. A comprehensive review and comparison of the first attempts considered for this task can be consulted in the work of [PERSON] et al. [20]. [PERSON] et al. [21] proposed a method that considers the staff lines as connected paths between the two margins of the score. The strategy was later improved and extended to be used on grayscale scores [22]. [PERSON] et al. [23] developed a method that considers the staff line segment as a horizontal connection of vertical black runs with a uniform height. [PERSON] et al. [24] estimated the properties of the staves in order to predict the direction of the lines. [PERSON] [25] developed a method that entails a series of morphological operators, which eventually removes staff lines in binary images. [PERSON] et al. [26] followed a similar approach but learning the parameters of the morphological image operators. [PERSON] et al. [27] used Support Vector Machines to classify pixels belonging to the categories staff or non-staff, which was later extended by incorporating the use of CNN [28] and Auto-Encoders [29]. Another relevant step related to the document processing of music score images is the segmentation of text regions. This layer needs to be detected as well, since it usually contains relevant  
Figure 1: Traditional workflow for Optical Music Recognition. information, for instance, metadata or lyrics. Although the automatic recognition of this information is usually performed by Optical Character Recognition (OCR) systems, the document processing stage is in charge of detecting the corresponding regions.