1. Iņesta, J.M.; Rizo, D.; Calvo-Zaragoza, J.

, vol. Proceedings of the 2nd Workshop on Reading Music Systems, WoRMS, pp. 12--15. Delft (The Nederlands) (2019)

The transcription process from historical hand- written music manuscripts to a structured digital encoding has been traditionally performed following a fully manual workflow. At most, it has received some technological support in particular stages, like optical music recognition (OMR) of the source images, or transcription to modern notation with music edition applications. Currently, there is no mature and stable enough solution for the OMR problem, and the most used music editors do not support early notations, such as the mensural notation. A new tool called MUsic Recognition, Encoding, and Transcription (MuRET) has been developed, which covers all transcription phases, from the manuscript image to the encoded digital content. MuRET is designed as a machine-learning based research tool, allowing different processing approaches to be used, and producing both the expected transcribed contents in standard encodings and data for the study of the transcription process.