Human-Interactive Optical Music Recognition

We propose a human-driven Optical Music Recognition (OMR) system that creates symbolic music data from common Western notation scores. Despite decades of development, OMR still remains largely unsolved as state-ofthe-art automatic systems are unable to give reliable and useful results on a wide range of documents. For this reason our system, Ceres, combines human input and machine recognition to efficiently generate high-quality symbolic data. We propose a scheme for human-in-the-loop recognition allowing the user to constrain the recognition in two ways. The human actions allow the user to impose either a pixel labeling or model constraint, while the system rerecognizes subject to these constraints. We present evaluation based on different users’ log data using both Ceres and Sibelius software to produce the same music documents. We conclude that our system shows promise for transcribing complicated music scores with high accuracy.