Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Gruber, Ivan | |
dc.contributor.author | Hrúz, Marek | |
dc.contributor.author | Ircing, Pavel | |
dc.contributor.author | Neduchal, Petr | |
dc.contributor.author | Zítka, Tomáš | |
dc.contributor.author | Hlaváč, Miroslav | |
dc.contributor.author | Zajíc, Zbyněk | |
dc.contributor.author | Švec, Jan | |
dc.contributor.author | Bulín, Martin | |
dc.date.accessioned | 2022-03-21T11:00:17Z | - |
dc.date.available | 2022-03-21T11:00:17Z | - |
dc.date.issued | 2021 | |
dc.identifier.citation | GRUBER, I. HRÚZ, M. IRCING, P. NEDUCHAL, P. ZÍTKA, T. HLAVÁČ, M. ZAJÍC, Z. ŠVEC, J. BULÍN, M. OCR Improvements for Images of Multi-page Historical Documents. In 23rd International Conference, SPECOM 2021, St. Petersburg, Russia, September 27–30, 2021, Proceedings. Cham: Springer, 2021. s. 226-237. ISBN: 978-3-030-87801-6 , ISSN: 0302-9743 | cs |
dc.identifier.isbn | 978-3-030-87801-6 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | 2-s2.0-85116373386 | |
dc.identifier.uri | http://hdl.handle.net/11025/47184 | |
dc.format | 12 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Springer | en |
dc.relation.ispartofseries | 23rd International Conference, SPECOM 2021, St. Petersburg, Russia, September 27–30, 2021, Proceedings | en |
dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
dc.rights | © Springer Nature Switzerland AG | en |
dc.title | OCR Improvements for Images of Multi-page Historical Documents | en |
dc.type | konferenční příspěvek | cs |
dc.type | ConferenceObject | en |
dc.rights.access | restrictedAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | This work presents a pipeline for processing digitally scanned documents, reading their textual content, and storing it in a dataset for the purpose of information retrieval. The pipeline is able to handle images of various quality, whether they were obtained by a digital scanner or camera. The image can contain multiple pages in any layout, but an approximate upright orientation is assumed. The pipeline uses Faster R-CNN to detect individual pages. These are then processed by a deskew algorithm to correct the orientation, and finally read by the Tesseract OCR system that has been retrained on a large set of synthetic images and a small set of annotated real-world documents. By applying the pipeline, we were able to increase the word recall to 60.56% which is an absolute gain of 19.19% from the baseline solution that uses only Tesseract OCR. A demo of the proposed pipeline can be found at https://archivkgb.zcu.cz/. | en |
dc.subject.translated | document digitization | en |
dc.subject.translated | document layout analysis | en |
dc.subject.translated | optical character recognition | en |
dc.subject.translated | image preprocessing | en |
dc.identifier.doi | 10.1007/978-3-030-87802-3_21 | |
dc.type.status | Peer-reviewed | en |
dc.identifier.obd | 43933456 | |
dc.project.ID | 90042/Velká výzkumná infrastruktura povinnost (J) - CESNET II | cs |
dc.project.ID | DG20P02OVV018/Digitální archiv dokumentů NKVD/KGB vztahujících se k Československu | cs |
Vyskytuje se v kolekcích: | Články / Articles (NTIS) Konferenční příspěvky / Conference Papers (KKY) OBD |
Soubory připojené k záznamu:
Soubor | Velikost | Formát | |
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Gruber2021_Chapter_OCRImprovementsForImagesOfMult.pdf | 2,27 MB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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http://hdl.handle.net/11025/47184
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