Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorHast, Anders
dc.contributor.authorVats, Ekta
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-05-07T06:33:47Z-
dc.date.available2019-05-07T06:33:47Z-
dc.date.issued2018
dc.identifier.citationJournal of WSCG. 2018, vol. 26, no. 1, p. 31-40.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.uriwscg.zcu.cz/WSCG2018/!_2018_Journal_WSCG-No-1.pdf
dc.identifier.urihttp://hdl.handle.net/11025/34586
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectFourierův deskriptor radiální linkycs
dc.subjectbodování slovcs
dc.subjectodpovídající funkcecs
dc.titleRadial line Fourier descriptor for historical handwritten text representationen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedAutomatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.en
dc.subject.translatedradial line Fourier descriptoren
dc.subject.translatedword spottingen
dc.subject.translatedfeature matchingen
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2018.26.1.4
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 26, Number 1 (2018)

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
Hast.pdfPlný text3,01 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/34586

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 26 (2018)
  6. Volume 26, Number 1 (2018)