Music Document Layout Analysis through Machine Learning and Human Feedback

Abstract

Music documents often include musical symbols as well as other relevant elements such as staff lines, text, and decorations. To detect and separate these constituent elements, we propose a layout analysis framework based on machine learning that focuses on pixel-level classification of the image. For that, we make use of supervised learning classifiers trained to infer the category of each pixel. In addition, our scenario considers a human-aided computing approach in which the user is part of the recognition loop, providing feedback where relevant errors are made.