Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41708
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dc.titleA fully automatic system for restoration of historical document images
dc.contributor.authorWang, J.
dc.contributor.authorBrown, M.S.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:33:51Z
dc.date.available2013-07-04T08:33:51Z
dc.date.issued2009
dc.identifier.citationWang, J.,Brown, M.S.,Tan, C.L. (2009). A fully automatic system for restoration of historical document images. Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference, IAAI-09 : 179-184. ScholarBank@NUS Repository.
dc.identifier.isbn9781577354239
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41708
dc.description.abstractHistorical document images are subject to intrinsic distortions such as background noise and bleed-through interference due to aging and extrinsic distortions such as displacement, uneven surfaces introduced during image acquisition procedure. In this paper, we propose a fully automatic restoration framework that corrects bleed-through distortion on double-sided handwritten historical document images. First, the two sides of a document are registered with corresponding control points which are selected by inspecting the images' gradient maps and minimizing a predefined dissimilarity measure. The established correspondences are refined by median filters and consistency checking. Piecewise linear mapping function is chosen to represent the spatial relationship between the two images. Based on the estimated transform model, backward re-sampling strategy and bi-cubic spline interpolation are adopted to obtain final registered images. Once the two sides of a page have been registered, enhancement/smearing feature images are extracted and iterative wavelet decomposition/construction is performed to restore the degraded images. Experiments on the real documents from the National Archives of Singapore demonstrate a completely automatic framework to the restoration of historical document images. Copyright © 2009.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the 21st Innovative Applications of Artificial Intelligence Conference, IAAI-09
dc.description.page179-184
dc.identifier.isiutNOT_IN_WOS
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