PageNet: Page Boundary Extraction in Historical Handwritten Documents

International Workshop on Historical Document Imaging and Processing

Published September 10, 2017

Chris Tensmeyer, Brian Davis, Curtis Wigington, Iain Lee, William Barrett

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 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 a 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 collections. Additionally, we show that PageNet can segment documents that are overlayed on top of other documents.

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