Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2005.219
DC FieldValue
dc.titleSkew estimation for scanned documents from "noises"
dc.contributor.authorYuan, B.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:11:26Z
dc.date.available2013-07-04T08:11:26Z
dc.date.issued2005
dc.identifier.citationYuan, B.,Tan, C.L. (2005). Skew estimation for scanned documents from "noises". Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 2005 : 277-281. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICDAR.2005.219" target="_blank">https://doi.org/10.1109/ICDAR.2005.219</a>
dc.identifier.isbn0769524206
dc.identifier.issn15205363
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40748
dc.description.abstractThe vast majority of the published skew estimation methods for scanned document images are for textual documents. These methods are based on the principle that the skew angles can be derived from the presence of the obvious text lines, The non-textual objects, such as line drawings, photographic inserts, scan artifacts including the dark bars around the borders and the center spine of bounded materials, and media contaminations are considered as "noises", thus are subject to elimination. Skew estimators that work in the presence of excessive noises are considered robust. This paper presents a skew estimation method that is based on the straight lines or edges. It uses the Muff Transform [15] with a probe-line mapping scheme for feature identification. Various strategies for optimized line probing are devised. This method is applicable to both textual and graphical documents scanned with ordinary scanners or copiers under normal conditions. Selected images from the University of Washington English Document Image Database I (UWDB-I) are used for its usability evaluation. © 2005 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDAR.2005.219
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICDAR.2005.219
dc.description.sourcetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.description.volume2005
dc.description.page277-281
dc.identifier.isiutNOT_IN_WOS
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