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Journal of Electronic Imaging

Text, photo, and line extraction in scanned documents
Author(s): M. Sezer Erkilinc; Mustafa I. Jaber; Eli Saber; Peter Bauer; Dejan Depalov
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Paper Abstract

We propose a page layout analysis algorithm to classify a scanned document into different regions such as text, photo, or strong lines. The proposed scheme consists of five modules. The first module performs several image preprocessing techniques such as image scaling, filtering, color space conversion, and gamma correction to enhance the scanned image quality and reduce the computation time in later stages. Text detection is applied in the second module wherein wavelet transform and run-length encoding are employed to generate and validate text regions, respectively. The third module uses a Markov random field based block-wise segmentation that employs a basis vector projection technique with maximum a posteriori probability optimization to detect photo regions. In the fourth module, methods for edge detection, edge linking, line-segment fitting, and Hough transform are utilized to detect strong edges and lines. In the last module, the resultant text, photo, and edge maps are combined to generate a page layout map using K-Means clustering. The proposed algorithm has been tested on several hundred documents that contain simple and complex page layout structures and contents such as articles, magazines, business cards, dictionaries, and newsletters, and compared against state-of-the-art page-segmentation techniques with benchmark performance. The results indicate that our methodology achieves an average of ∼ 89% classification accuracy in text, photo, and background regions.

Paper Details

Date Published: 13 July 2012
PDF: 19 pages
J. Electron. Imag. 21(3) 033006 doi: 10.1117/1.JEI.21.3.033006
Published in: Journal of Electronic Imaging Volume 21, Issue 3
Show Author Affiliations
M. Sezer Erkilinc, Univ. College London (United Kingdom)
Mustafa I. Jaber, IPPLEX Holdings Corp. (United States)
Eli Saber, Rochester Institute of Technology (United States)
Peter Bauer, Hewlett-Packard Co. (United States)
Dejan Depalov, Hewlett-Packard Co. (United States)

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