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Document page classification algorithms in low-end copy pipeline

[+] Author Affiliations
Xiaogang Dong

Sony Electronics Inc., Media Processing Technology Laboratory, San Jose, California 95112

Kai-Lung Hua

Purdue University, School of Electrical and Computer Engineering, West Lafayette, Indiana 47907

Peter Majewicz

Hewlett-Packard Company, Boise, Idaho 83714

Gordon McNutt

CradlePoint, Inc., Boise, Idaho 83702

Charles A. Bouman

Purdue University, School of Electrical and Computer Engineering, West Lafayette, Indiana 47907

Jan P. Allebach

Purdue University, School of Electrical and Computer Engineering, West Lafayette, Indiana 47907

Ilya Pollak

Purdue University, School of Electrical and Computer Engineering, West Lafayette, Indiana 47907

J. Electron. Imaging. 17(4), 043011 (November 12, 2008). doi:10.1117/1.3010879
History: Received August 14, 2007; Revised September 02, 2008; Accepted September 04, 2008; Published November 12, 2008
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We develop real-time, low-complexity image classification algorithms suitable for a copy mode selector embedded in a low-end copier. The algorithms classify scanned images represented in RGB or in an opponent color space. Classes are the eight combinations of mono/color and text/mix/picture/photo. Classification is 30–98% accurate with misclassifications tending to be benign. The algorithms provide for improved copy quality, a simplified user interface, and increased copy rate.

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Citation

Xiaogang Dong ; Kai-Lung Hua ; Peter Majewicz ; Gordon McNutt ; Charles A. Bouman, et al.
"Document page classification algorithms in low-end copy pipeline", J. Electron. Imaging. 17(4), 043011 (November 12, 2008). ; http://dx.doi.org/10.1117/1.3010879


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