Optical Music Recognition (OMR) is the field of research
that studies how to computationally read music notation
from written documents. Thanks to recent advances in
computer vision and deep learning, there are successful approaches
that can locate the music-notation elements from
a given music score image. Once detected, these elements
must be related to each other to reconstruct the musical
notation itself, in the so-called notation assembly stage.
However, despite its relevance in the ...
Optical Music Recognition (OMR) is the field of research
that studies how to computationally read music notation
from written documents. Thanks to recent advances in
computer vision and deep learning, there are successful approaches
that can locate the music-notation elements from
a given music score image. Once detected, these elements
must be related to each other to reconstruct the musical
notation itself, in the so-called notation assembly stage.
However, despite its relevance in the eventual success of
the OMR, this stage has been barely addressed in the literature.
This work presents a set of neural approaches to perform
this assembly stage. Taking into account the number
of possible syntactic relationships in a music score, we give
special importance to the efficiency of the process in order
to obtain useful models in practice. Our experiments, using
the MUSCIMA++ handwritten sheet music dataset, show
that the considered approaches are capable of outperforming
the existing state of the art in terms of efficiency with
limited (or no) performance degradation. We believe that
the conclusions of this work provide novel insights into
the notation assembly step, while indicating clues on how
to approach the previous stages of the OMR and improve
the overall performance.
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