Staff line Removal using Generative Adversarial Networks
This addresses a crucial pre-processing step for Optical Music Recognition, specifically for ancient degraded scores, but is incremental as it applies existing GAN techniques to a domain-specific task.
The paper tackles the problem of staff line removal in degraded ancient music score images by proposing a Generative Adversarial Network (GAN) approach, achieving superior performance compared to conventional methods on the ICDAR/GREC 2013 database.
Staff line removal is a crucial pre-processing step in Optical Music Recognition. It is a challenging task to simultaneously reduce the noise and also retain the quality of music symbol context in ancient degraded music score images. In this paper we propose a novel approach for staff line removal, based on Generative Adversarial Networks. We convert staff line images into patches and feed them into a U-Net, used as Generator. The Generator intends to produce staff-less images at the output. Then the Discriminator does binary classification and differentiates between the generated fake staff-less image and real ground truth staff less image. For training, we use a Loss function which is a weighted combination of L2 loss and Adversarial loss. L2 loss minimizes the difference between real and fake staff-less image. Adversarial loss helps to retrieve more high quality textures in generated images. Thus our architecture supports solutions which are closer to ground truth and it reflects in our results. For evaluation we consider the ICDAR/GREC 2013 staff removal database. Our method achieves superior performance in comparison to other conventional approaches.