## 1 Introduction  
Music is one of the cornerstones of cultural heritage [1]. Throughout history, the main means of transmitting and preserving this art has been its engravement in so-called music scores, i.e., documents in which music composers graphically encode a piece of music as well as the way to perform it [2]. This particular visual language is identified as music notation and is known to be considerably different depending on epoch, culture, instrumentation, and genre, among many others [3]. This engraving process was carried out manually until the end of the 20 th century, mostly done by handwriting or typesetting processes; thus, millions of music documents are only available as physical documents [4]. While large quantities of them have been scanned into digital images, only a few hundreds have been stored in a structured digital format that allows tasks such as indexing, editing, or critical publication [5]. Since its manual transcription constitutes a costly, tedious, and prone-to-error task, the development of systems capable of automatically performing this process is of remarkable importance, even more so considering the vast amount of existing music archives [6]. Optical Music Recognition (OMR) is the research field that investigates how to computationally read music notation in printed documents and to store them in a digital structured format [7]. One of the main challenges in this field lies in the aforementioned heterogeneity of the documents, especially when considering the different notation styles present in historical documents [8]. Thus, while OMR has been an active research area for decades [9, 10],such particularity prevents the field from providing general developments for any type of document. Traditionally, most OMR systems rely on a multi-stage pipeline, which, in a broad sense, is devoted to segmenting the complete score into single units (e.g., staves or symbols), which are eventually recognized [11] using traditional machine learning or signal processing techniques. However, it must be noted that such approaches achieve competitive recognition rates at the expense of using certain heuristics adequately designed for a particular notation and writing/printing style, being only applicable to the case for which they were devised [12]. In this regard, this framework becomes considerably limited in terms of scalability given that a new pipeline and a set of heuristics must be designed for each particular document and/or notation. Recent developments in the so-called _deep learning_ paradigm have led to a considerable renewal of learning-based approaches [13]. This area represents the current state-of-the-art in a wide range of applications, with _image classification_ being a particular example in which traditional machine learning techniques seemed to have reached a glass ceiling [14]. Hence, it is not strange that OMR has severely benefited from that field to somehow palliate its issues. One of the key advantages of deep learning is that it allows for the creation of the so-called _holistic_ or _end-to-end_ neural solutions to tackle recognition tasks, i.e., systems in which both feature extraction and classification processes are learned jointly [15]. This particularity prevents the need for designing an entire pipeline for each particular case of study since the traditionally hand-crafted tasks are directly inferred from the data itself. Such a functionality has been largely explored in the literature, with OMR being a case in which it has clearly proven its usefulness [16], at least in symbol recognition at the isolated staff level. It must be noted that, while generative methods based on Hidden Markov Models have also been considered for holistic OMR as in the works by [PERSON] [17] or [PERSON] et al. [18], the performance of the commented neural methods outperforms them. Nevertheless, there is still room for improvement in neural-based holistic OMR by exploiting the inherent particularities of this type of data, which have not been totally explored.