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This paper presents a Convolutional Neural Network (CNN) based pagesegmentation method for handwritten historical document images. We considerpage segmentation as a pixel labeling problem, i.e., each pixel is classifiedas one of the predefined classes. Traditional methods in this area rely oncarefully hand-crafted features or large amounts of prior knowledge. Incontrast, we propose to learn features from raw image pixels using a CNN. Whilemany researchers focus on developing deep CNN architectures to solve differentproblems, we train a simple CNN with only one convolution layer. We show thatthe simple architecture achieves competitive results against other deeparchitectures on different public datasets. Experiments also demonstrate theeffectiveness and superiority of the proposed method compared to previousmethods.

Convolutional Neural Networks for Page Segmentation of Historical Document Images