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arXiv · 1709.01618

PageNet: Page Boundary Extraction in Historical Handwritten Documents

Chris Tensmeyer·Brian Davis·Curtis Wigington·Iain Lee·Bill Barrett
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Abstract

When digitizing a document into an image, it is common to include a surrounding border region to visually indicate that the entire document is present in the image. However, this border should be removed prior to automated processing. In this work, we present a deep learning based system, PageNet, which identifies the main page region in an image in order to segment content from both textual and non-textual border noise. In PageNet, a Fully Convolutional Network obtains a pixel-wise segmentation which is post-processed into the output quadrilateral region. We evaluate PageNet on 4 collections of historical handwritten documents and obtain over 94% mean intersection over union on all datasets and approach human performance on 2 of these collections. Additionally, we show that PageNet can segment documents that are overlayed on top of other documents.

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Chris Tensmeyer, Brian Davis, Curtis Wigington, Iain Lee, Bill Barrett. 2017-09-05. PageNet: Page Boundary Extraction in Historical Handwritten Documents. https://arxiv.org/abs/1709.01618

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