# PageNet: Page Boundary Extraction in Historical Handwritten Documents

arXiv: https://arxiv.org/abs/1709.01618 · HTML: https://mlsanity.com/paper/1709.01618/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Confidence Estimation for Object Detection in Document Images](https://mlsanity.com/paper/2208.13391/) (2022)
- [Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts](https://mlsanity.com/paper/2312.04296/) (2023)

## Abstract (arXiv)

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.