A Deep Approach for Handwritten Musical Symbols Recognition
Roberto Pereira, Caio Eduardo Falcão Matos, Geraldo Bráz, João Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva · 2016
Preserving the world musical heritage comes down to digitalising and provision of music works to further query on the acquired data. However, to do the processing it is necessary an Optical Music Recognition (OMR) system capable of decoding the original manuscripts into a machine-readable data. Developing a precise and robust OMR system for handwritten musical scores is still an open issue. A fundamental step of improve such task is to recognise musical notes. Hence, trying to provide ways to produce a truly robust OMR sys- tem, we present in this paper a new methodology applying deep learning techniques to recognise musical notes in digitalised handwritten musical scores. The proposed methodology has been tested on a ground truth dataset of music scores reaching a minimum error rate of 3.99%, 96.46% of precision and 96.56% of recall on the HOMUS dataset.