A Deep Approach for Handwritten Musical Symbols Recognition - Pereira, Roberto M. Pinheiro; Matos, Caio Eduardo Falcao; Junior, Geraldo Braz; Almeida, João Dallyson Sousa de; Paiva, Anselmo Cardoso de (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2016: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, 2016)
Preserving the world musical heritage comes down to digitalizing 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 system, 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.