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김광인

Kim, Kwang In
Machine Learning and Vision Lab.
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DC Field Value Language
dc.citation.endPage 997 -
dc.citation.number 5 -
dc.citation.startPage 977 -
dc.citation.title PATTERN RECOGNITION -
dc.citation.volume 37 -
dc.contributor.author Jung, Keechul -
dc.contributor.author Kim, Kwnag In -
dc.contributor.author Jain, Anil K. -
dc.date.accessioned 2023-12-22T11:06:19Z -
dc.date.available 2023-12-22T11:06:19Z -
dc.date.created 2019-02-25 -
dc.date.issued 2004-05 -
dc.description.abstract Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. -
dc.identifier.bibliographicCitation PATTERN RECOGNITION, v.37, no.5, pp.977 - 997 -
dc.identifier.doi 10.1016/j.patcog.2003.10.012 -
dc.identifier.issn 0031-3203 -
dc.identifier.scopusid 2-s2.0-1842712330 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/26217 -
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S0031320303004175?via%3Dihub -
dc.identifier.wosid 000220677200010 -
dc.language 영어 -
dc.publisher ELSEVIER SCI LTD -
dc.title Text information extraction in images and video: a survey -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic -
dc.relation.journalResearchArea Computer Science; Engineering -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor text information extraction -
dc.subject.keywordAuthor text detection -
dc.subject.keywordAuthor text localization -
dc.subject.keywordAuthor text tracking -
dc.subject.keywordAuthor text enhancement -
dc.subject.keywordAuthor OCR -
dc.subject.keywordPlus SCENE IMAGES -
dc.subject.keywordPlus COLOR DOCUMENTS -
dc.subject.keywordPlus LICENSE PLATES -
dc.subject.keywordPlus DIGITAL VIDEO -
dc.subject.keywordPlus RECOGNITION -
dc.subject.keywordPlus RETRIEVAL -
dc.subject.keywordPlus CHARACTERS -
dc.subject.keywordPlus LOCATION -
dc.subject.keywordPlus LOCALIZATION -
dc.subject.keywordPlus SEGMENTATION -

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