Binarization of degraded document images with global-local U-Nets

摘要

Document binarization plays a significant role in the document analysis to extract foreground text from the background. Traditional Convolutional Neural Networks (CNNs) focus only on local textual features and ignore global context, which are both important for pixel classification in segmentation based document binarization. In this paper, we propose a local-global combined approach for document binarization. This model is composed of a global branch and a local branch, taking the global patches from downsampled image and cropped local patches from source image as respective inputs. The final binary prediction is achieved via combining the results of this two branches. The experimental results on several DIBCO datasets present that our method outperforms many traditional and state-of-the-art document binarization algorithms.