Unsupervised Domain Adaptation for Document Analysis of Music Score Images
Francisco J. Castellanos, Antonio‐Javier Gallego, Jorge Calvo-Zaragoza · Zenodo (CERN European Organization for Nuclear Research) · 2021
Document analysis is a key step within the typical Optical Music Recognition workflow. It processes an input image to obtain its layered version by extracting the different sources of information. Recently, this task has been formulated as a supervised learning problem, specifically by means of Convolutional Neural Networks due to their high performance and generalization capability. However, the requirement of training data for each new type of document still represents an important drawback. This issue can be palliated through Domain Adaptation (DA), which is the field that aims to adapt the knowledge learned with an annotated collection of data to other domains for which labels are not available. In this work, we combine a DA strategy based on adversarial training with Selectional Auto-Encoders to define an unsupervised framework for document analysis. Our experiments show a remarkable improvement for the layers that depict particular features at each domain, whereas layers that depict common features (such as staff lines) are barely affected by the adaptation process. In the best-case scenario, our method achieves an average relative improvement of around 44%, thereby representing a promising solution to unsupervised document analysis.