Music Document Layout Analysis through Machine Learning and Human Feedback
Jorge Calvo-Zaragoza, Ke Zhang, Zeyad Saleh, Gabriel Vigliensoni, Ichiro Fujinaga · 2017
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.