Title: Convolutional Neural Networks for Page Segmentation of Historical Document Images,
Abstract: This paper presents a Convolutional Neural Network (CNN) based page
segmentation method for handwritten historical document images. We consider
page segmentation as a pixel labeling problem, i.e., each pixel is classified
as one of the predefined classes. Traditional methods in this area rely on
carefully hand-crafted features or large amounts of prior knowledge. In
contrast, we propose to learn features from raw image pixels using a CNN. While
many researchers focus on developing deep CNN architectures to solve different
problems, we train a simple CNN with only one convolution layer. We show that
the simple architecture achieves competitive results against other deep
architectures on different public datasets. Experiments also demonstrate the
effectiveness and superiority of the proposed method compared to previous
methods.