Robust extraction of marked-up text sections from scientific document printouts
- We present a simple tool for extracting text and markup information from printouts of (not only) scientific documents. While the heavy-lifting OCR is done by off-the-shelf tesseract, our focus is on detection, extraction, and basic categorization of color-highlighted text sections, as well as on providing a framework for downstream processing of extraction results. The tool can be useful for document analysis tasks that must, or benefit from being able to, use printed paper.
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| Author: | Mark-Christoph MüllerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-124275 |
| URL: | https://iapr.org/archives/das2022/wp-content/uploads/2022/05/DAS2022_short-paper-booklet.pdf |
| Parent Title (English): | 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022 |
| Publisher: | La Rochelle University |
| Place of publication: | La Rochelle |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2022 |
| Date of Publication (online): | 2024/01/09 |
| Publishing Institution: | Leibniz-Institut für Deutsche Sprache (IDS) |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| Tag: | Document Images; Information Extraction; Multi-modality; Natural Language Processing; OCR |
| GND Keyword: | Dokument; Kategorisierung; Optische Zeichenerkennung; Schriftstück; Texttechnologie |
| First Page: | 32 |
| Last Page: | 35 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Textlinguistik / Schriftsprache |
| Licence (German): |  Urheberrechtlich geschützt |