The recognition of patterns that have a time dependency is common in areas like speech recognition or natural language processing. The equivalent situation in image analysis is present in tasks like text or video recognition. Recently, Convolutional Recurrent Neural Networks (CRNN) have been broadly applied to solve these tasks in an end-to-end fashion with successful performance. However, its application to Optical Music Recognition (OMR) is not so straightforward due to the presence of different elements sharing the same horizontal position, disrupting the linear flow of the timeline. In this paper, we study the ability of the state-of-the-art CRNN approach to learn codes that represent this disruption in homophonic scores. In our experiments, we study the lower bounds in the recognition task of real scores when the models are trained with synthetic data. Two relevant conclusions are drawn: (1) Our serialized ways of encoding the music content are appropriate for CRNN-based OMR; (2) the learning process is possible with synthetic data, but there exists a glass ceiling when recognizing real sheet music.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project, funded by MCIN/AEI/10.13039/501100011033. The first author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades
