{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pagenet-page-boundary-extraction-in","title":"PageNet: Page Boundary Extraction in Historical Handwritten Documents","arxiv_id":"1709.01618","date":"2017-09-05","proceeding":null,"authors":["Chris Tensmeyer","Brian Davis","Curtis Wigington","Iain Lee","Bill Barrett"],"abstract":"When digitizing a document into an image, it is common to include a\nsurrounding border region to visually indicate that the entire document is\npresent in the image. However, this border should be removed prior to automated\nprocessing. In this work, we present a deep learning based system, PageNet,\nwhich identifies the main page region in an image in order to segment content\nfrom both textual and non-textual border noise. In PageNet, a Fully\nConvolutional Network obtains a pixel-wise segmentation which is post-processed\ninto the output quadrilateral region. We evaluate PageNet on 4 collections of\nhistorical handwritten documents and obtain over 94% mean intersection over\nunion on all datasets and approach human performance on 2 of these collections.\nAdditionally, we show that PageNet can segment documents that are overlayed on\ntop of other documents.","url_abs":"http://arxiv.org/abs/1709.01618v1","url_pdf":"http://arxiv.org/pdf/1709.01618v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pagenet-page-boundary-extraction-in","repo_url":"https://github.com/ctensmeyer/pagenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"pagenet-page-boundary-extraction-in","repo_url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PAGENet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"pagenet-page-boundary-extraction-in","repo_url":"https://github.com/MS-Mind/MS-Code-06/tree/main/PAGENet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}