  Optical Music Recognition (OMR) has long been without an adequate dataset and
ground truth for evaluating OMR systems, which has been a major problem for
establishing a state of the art in the field. Furthermore, machine learning
methods require training data. We analyze how the OMR processing pipeline can
be expressed in terms of gradually more complex ground truth, and based on this
analysis, we design the MUSCIMA++ dataset of handwritten music notation that
addresses musical symbol recognition and notation reconstruction. The MUSCIMA++
dataset version 0.9 consists of 140 pages of handwritten music, with 91255
manually annotated notation symbols and 82261 explicitly marked relationships
between symbol pairs. The dataset allows training and evaluating models for
symbol classification, symbol localization, and notation graph assembly, both
in isolation and jointly. Open-source tools are provided for manipulating the
dataset, visualizing the data and further annotation, and the dataset itself is
made available under an open license.
