Abstract:
Optical Music Recognition (OMR) is an important task in music information retrieval from music sheet images. Precise detection of music symbols
in a music sheet image is an essential component of any OMR system. The
approaches that are currently being used do not show promising results on
rare symbols. Additionally, an image of a music sheet contains a large number of densely packed symbols. This makes detection of smaller symbols
using traditional approaches, which have been designed to detect fewer and
larger objects in the images, difficult. In order to address these problems, a
two-step approach for Music Symbol Detection has been presented. In the
first step, the locations of the composite symbols has been identified instead
of locating symbols primitives using a deep learning based approach inspired
by state-of-the-art scene text detection method. In the second step, region
based convolutional neural networks (R-CNN) has been used to detect symbol primitives in the image crops obtained from the localization step. The
proposed approach targets the music sheets that contain a large number of
music symbols. Additionally, primitive level symbol detection on image crops
instead of full images allows us to sample rare symbols more often during the
training process and helps to offset the effect of symbol imbalance in the
data. The proposed approach has shown mAP of 24.9 and wmAP of 48.74
on DeepScores Dense Extended dataset [1]. Thus, this thesis makes twofold contribution through our work: (i) introducing a two-step music symbol
detection algorithm and (ii) establishing baseline results with DeepScore Extended dataset.
