# End-to-End Neural Optical Music Recognition of Monophonic Scores
## 6 Conclusions  
In this work, we have studied the suitability of the use of the neural network approach to solve the OMR task in an end-to-end fashion through a controlled scenario of printed single-staff monodic scores from a real world dataset. The neural network used makes use of both convolutions and recurrent blocks, which are responsible of dealing with the graphic and sequential parts of the problem, respectively. This is combined with the use of the so-called CTC loss function that allows us to train the model in a less demanding way: only pairs of images and their corresponding transcripts, without any geometric information about the position of the symbols or their composition from simple primitives. In addition to this approach, we also present the Printed Images of Music Staves (PrIMuS) dataset for use in experiments. Specifically, PrIMuS is a collection of 87 678 incipits extracted from the RISM repository and rendered with various fonts using the Verovio tool. The main contribution of the present work consisted of analyzing the possible codifications that can be considered for representing the expected output. In this paper we have proposed and studied two options: an agnostic representation, in which only the graphical point of view is taken into account, and a semantic representation, which codifies the symbols according to their musical meaning. Our experiments have reported several interesting conclusions:  
* The task can be successfully solved using the considered neural end-to-end approach. Figure 11: Incipit RISM ID no. 00010016. _Es ist das Heil uns kommen her_. [PERSON]. Both Photoscore and our system perfectly recognize the music content within the image. **(a)** Photoscore output: The sample is perfectly recognized; **(b)** Output of our system: The sample is perfectly recognized. * The semantic representation that includes musical meaning symbols has a superior glass ceiling of performance, visibly improving the results obtained using the agnostic representation. * In general, errors occur in those symbols with less representation in the training set. * This approach allows a performance comparable to that of commercial systems. * The learning process with the agnostic representation made up of just graphic symbols is simpler, since the neural model converges faster and the learning curve is more pronounced than those with the semantic representation. * Regardless of the representation, the neural model is not able to locate the symbols in the image--which could be expected because of the way the CTC loss function operates. As future work, this work opens many possibilities for further research. For instance, it would be interesting to study the neural approach in a more general scenario in which the scores are not perfectly segmented into staves or in non-ideal conditions at the document level (irregular lighting, geometric distortions, bleed-through, etc.). However, it is undoubted that the most promising avenue is to extend the neural approach so that it is capable of dealing with a comprehensive set of notation symbols, including articulation and dynamic marks, as well as with multiple-voice polyphonic staves. We have seen in PrIMuS that there are several symbols that may appear simultaneously (like the numbers of a time signature), and the neural model is able to deal with them. However, it is clear that polyphony, both at the single staff level (eg., chords) or at the system level, represents the main challenge to advance in the OMR field. Concerning the most technical aspect, it would be interesting to study a multi-prediction model that uses all the different representations at the same time. Given the complementarity of these representations, it is feasible to think of establishing a synergy that ends up with better results in all senses. This work was supported by the Social Sciences and Humanities Research Council of Canada, and the Spanish Ministerio de Economia y Competitividad through Project HISPMUS Ref. No. TIN2017-86576-R (supported by UE FEDER funds).