Staff line Removal using Generative Adversarial Networks

TL;DR

This paper introduces a novel GAN-based method for staff line removal in musical scores, improving the quality of staff-less images by combining U-Net and adversarial training, outperforming traditional techniques.

Contribution

The paper presents a new GAN architecture utilizing U-Net for staff line removal, enhancing image quality and accuracy in optical music recognition preprocessing.

Findings

Achieves superior performance on ICDAR/GREC 2013 database.

Effectively preserves music symbol details in staff-less images.

Outperforms conventional staff line removal methods.

Abstract

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.