  Optical Music Recognition is a field that has progressed significantly,
bringing accurate systems that transcribe effectively music scores into digital
formats. Despite this, there are still several limitations that hinder OMR from
achieving its full potential. Specifically, state of the art OMR still depends
on multi-stage pipelines for performing full-page transcription, as well as it
has only been demonstrated in monophonic cases, leaving behind very relevant
engravings. In this work, we present the Sheet Music Transformer++, an
end-to-end model that is able to transcribe full-page polyphonic music scores
without the need of a previous Layout Analysis step. This is done thanks to an
extensive curriculum learning-based pretraining with synthetic data generation.
We conduct several experiments on a full-page extension of a public polyphonic
transcription dataset. The experimental outcomes confirm that the model is
competent at transcribing full-page pianoform scores, marking a noteworthy
milestone in end-to-end OMR transcription.
