## 7 Conclusions  
Holistic symbol recognition approaches have proven their usefulness in the context of Optical Music Recognition (OMR) since, given that no alignment is required between the input score and the sequence of output elements, corpora are relatively easy to create. In such a context, these sets are commonly labeled using either a _semantic_ notation, which codifies the actual musical meaning of each element in the score, or an _agnostic_ representation, which encodes the elements as a combination of a shape tag and the vertical position (height) in the score. While this latter representation lacks the underlying music sense of the former, it has the clear advantage of perfectly suiting an image-based symbol recognition task as the vocabulary is defined directly on visual information. However, in spite of having been considered for a number of work, it is still unclear how to properly take advantage of the fact that each symbol is actually a combination of two individual primitives representing the shape of the element and its height. This work presents an end-to-end approach that exploits this two-dimensional nature of the agnostic music notation to solve the OMR task at a staff-line level. We considered two Convolutional Recurrent Neural Network (CRNN) schemes to concurrently exploit the shape and height information and to then merge them at the actual neural level. Three different integration policies were empirically studied: (i) the _PreRNN_ one, which joins the shape and height features right before the recurrent block; (ii) the _InterRNN_ one, which does the merging process after the first recurrent layer; and (iii) the _PostRNN_ one, which collects both sources of information after the recurrent block. The results obtained confirm that the gathering point impacts the performance of the model, with the _InterRNN_ and _PostRNN_ models being the ones that significantly decrease the error rate with respect to the baseline considered. Quantitatively, in the best-case scenarios, this error reduction ranges between 14.4% and 25.6% referred to the base neural model. In light of the obtained conclusions, this work opens new research points to address. In that sense, future work considers extending the approach from this monophonic context to a homophonic one [42]. Additionally, given the variability of music notation and the relative scarcity of existing labeled data, we aim to explore _transfer learning_ and _domain adaptation_ techniques to study different strategies to properly exploit the knowledge gathered from a given corpus on a different one. Moreover, we also consider that other network architectures may provide some additional insights to the ones obtained in this work as well as more competitive recognition rates. Finally, since the _greedy_ decoding strategy considered does not take advantage of the actual Language Model inferred in the recurrent layer of the network, our premise is that more sophisticated decoding policies may report improvements in the overall performance of the proposal. Conceptualization, M.A.-C. and J.J.V.-M.; methodology, M.A.-C. and J.J.V.-M.; software, M.A.-C. and J.J.V.-M.; validation, M.A.-C. and J.J.V.-M.; formal analysis, M.A.-C. and J.J.V.-M.; writing, M.A.-C. and J.J.V.-M. All authors have read and agreed to the published version of the manuscript. This research work was partially funded by the University of Alicante through project GRE19-04, by the \"Programa I+D+i de la Generalitat Valenciana\" through grant APOSTD/2020/256, and by the Spanish Ministerio de Universidades through grant FPU19/04957. Not applicable. The data considered for this work is already available from their respective cited sources. No new data was generated in this work. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: