Exploring the two-dimensional nature of music notation for score recognition with end-to-end approaches

Optical Music Recognition workflows perform several steps to retrieve the content in music score images, being symbol recognition one of the key stages. State-of-the-art approaches for this stage currently address the coding of the output symbols as if they were plain text characters. However, music symbols have a two-dimensional nature that is ignored in these approaches. In this paper, we explore alternative output representations to perform music symbol recognition with state-of-the-art end-to-end neural technologies. We propose and describe new output representations which take into account the mentioned two-dimensional nature. We seek answers to the question of whether it is possible to obtain better recognition results in both printed and handwritten music scores. In this analysis, we compare the results given using three output encodings and two neural approaches. We found that one of the proposed encodings outperforms the results obtained by the standard one. This permits us to conclude that it is interesting to keep researching on this topic to improve end-to-end music score recognition.