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Noteheads are the interface between the written score and music. Eachnotehead on the page signifies one note to be played, and detecting noteheadsis thus an unavoidable step for Optical Music Recognition. Noteheads areclearly distinct objects, however, the variety of music notation handwritingmakes noteheads harder to identify, and while handwritten music notation symbol{\em classification} is a well-studied task, symbol {\em detection} has usuallybeen limited to heuristics and rule-based systems instead of machine learningmethods better suited to deal with the uncertainties in handwriting. We presentongoing work on a simple notehead detector using convolutional neural networksfor pixel classification and bounding box regression that achieves a detectionf-score of 0.97 on binary score images in the MUSCIMA++ dataset, does notrequire staff removal, and is applicable to a variety of handwriting styles andlevels of musical complexity.

Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression