Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41050
DC FieldValue
dc.titleLanguage identification in degraded and distorted document images
dc.contributor.authorLu, S.
dc.contributor.authorTan, C.L.
dc.contributor.authorHuang, W.
dc.date.accessioned2013-07-04T08:18:27Z
dc.date.available2013-07-04T08:18:27Z
dc.date.issued2006
dc.identifier.citationLu, S.,Tan, C.L.,Huang, W. (2006). Language identification in degraded and distorted document images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3872 LNCS : 232-242. ScholarBank@NUS Repository.
dc.identifier.isbn3540321403
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41050
dc.description.abstractThis paper presents a language identification technique that differentiates Latin-based languages in degraded and distorted document images. Different from the reported methods that transform word images through a character shape coding process, our method directly captures word shapes with the local extremum points and the horizontal intersection numbers, which are both tolerant of noise, character segmentation errors, and slight skew distortions. For each language studied, a word shape template and a word frequency template are firstly constructed based on the proposed word shape coding scheme. Identification is then accomplished based on Bray Curtis or Hamming distance between the word shape code of query images and the constructed word shape and frequency templates. Experiments show the average identification rate upon eight Latin-based languages reaches over 99%. © Springer-Verlag Berlin Heidelberg 2006.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3872 LNCS
dc.description.page232-242
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Page view(s)

112
checked on Nov 24, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.