{"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/multi-modal-page-stream-segmentation-with","title":"Multi-modal Page Stream Segmentation with Convolutional Neural Networks","arxiv_id":null,"date":"2019-09-27","proceeding":"Lang Resources & Evaluation 2019 9","authors":["Gregor Wiedemann","Gerhard Heyer"],"abstract":"In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page documents, the preservation of document contexts is a major requirement. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach for PSS based on convolutional neural networks (CNN). As a first project, we combine visual information from scanned images with semantic information from OCR-ed texts for this task. The multi-modal combination of features in a single classification architecture allows for major improvements towards optimal document separation. Further to multimodality, our PSS approach profits from transfer-learning and sequential page modeling. We achieve accuracy up to 95% on multi-page documents on our in-house dataset and up to 93% on a publicly available dataset.","url_abs":"https://link.springer.com/article/10.1007/s10579-019-09476-2","url_pdf":"https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.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":"multi-modal-page-stream-segmentation-with","repo_url":"https://github.com/uhh-lt/pss-lrev","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"},{"task_slug":"page-stream-segmentation","task_name":"Page Stream Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}