SEGMENTATION AND CLASSIFICATION OF MIXED TEXT/GRAPHICS/IMAGE DOCUMENTS

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > journal & Dissertation > Item 987654321/29241

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29241

Title:	SEGMENTATION AND CLASSIFICATION OF MIXED TEXT/GRAPHICS/IMAGE DOCUMENTS
Authors:	FAN,KC;LIU,CH;WANG,YK
Contributors:	資訊工程研究所
Date:	1994
Issue Date:	2010-06-29 20:17:17 (UTC+8)
Publisher:	中央大學
Abstract:	In this paper, a feature-based document analysis system is presented which utilizes domain knowledge to segment and classify mixed text/graphics/image documents. In our approach, we first perform a run-length smearing operation followed by the stripe merging procedure to segment the blocks embedded in a document. The classification task is then performed based on the domain knowledge induced from the primitives associated with each type of medium. Proper use of domain knowledge is proved to be effective in accelerating the segmentation speed and decreasing the classification error. The experimental study reveals the feasibility of the new technique in segmenting and classifying mixed text/graphics/image documents.
Relation:	PATTERN RECOGNITION LETTERS
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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