Pixel-wise binarization of musical documents with convolutional neural networks
Jorge Calvo-Zaragoza, Gabriel Vigliensoni, Ichiro Fujinaga · 2017
Binarization is an important process in document analysis systems. Yet, it is quite difficult to devise a binarization method that perform successfully over a wide range of documents, especially in the case of digitized old musical manuscripts and scores with irregular lighting and source degradation. Our approach to binarization of musical documents is based on training a Convolutional Neural Network that classifies each pixel of the image as either background or foreground. Our results demonstrate that the approach is competitive with other state-of-the-art algorithms. It also illustrates the advantage of being able to adapt to any type of score by simply modifying the training set.