Publication:
Improved wolf algorithm on document images detection using optimum mean technique

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cris.author.scopus-author-id 57219421621
cris.author.scopus-author-id 57202264868
cris.author.scopus-author-id 24824279500
dc.contributor.author Mustafa W.A.
dc.contributor.author Kader M.M.M.A.
dc.contributor.author Khalib Z.I.A.
dc.date.accessioned 2024-12-12T08:41:53Z
dc.date.available 2024-12-12T08:41:53Z
dc.date.issued 2019-06-01
dc.description.abstract Detection text from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. In this paper, a proposed method based on the optimum threshold value and namely as the Optimum Mean method was presented. Besides, Wolf method unsuccessful in order to detect the thin text in the non-uniform input image. However, the proposed method was suggested to overcome the Wolf method problem by suggesting a maximum threshold value using optimum mean. Based on the calculation, the proposed method obtained a higher F-measure (74.53), PSNR (14.77) and lowest NRM (0.11) compared to the Wolf method. In conclusion, the proposed method successful and effective to solve the wolf problem by producing a high-quality output image.
dc.identifier.doi 10.11591/eei.v8i2.1426
dc.identifier.scopus 2-s2.0-85071363653
dc.identifier.uri https://hdl.handle.net/20.500.14170/10262
dc.relation.funding Universiti Malaysia Perlis
dc.relation.grantno undefined
dc.relation.ispartof Bulletin of Electrical Engineering and Informatics
dc.relation.ispartofseries Bulletin of Electrical Engineering and Informatics
dc.relation.issn 20893191
dc.rights open access
dc.subject Binarization | Document | Mean | Optimum | Wolf
dc.title Improved wolf algorithm on document images detection using optimum mean technique
dc.type Journal
dspace.entity.type Publication
oaire.citation.endPage 557
oaire.citation.issue 2
oaire.citation.startPage 551
oaire.citation.volume 8
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
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person.identifier.scopus-author-id 57219421621
person.identifier.scopus-author-id 57202264868
person.identifier.scopus-author-id 24824279500
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