SVM-based automatic scanned image classification with quick decision capability

Cheng Lu; Jerry Wagner; Brandi Pitta; David Larson; Jan Allebach

doi:10.1117/12.2047335

3 February 2014 SVM-based automatic scanned image classification with quick decision capability

Cheng Lu, Jerry Wagner, Brandi Pitta, David Larson, Jan Allebach

Proceedings Volume 9015, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications; 90150G (2014) https://doi.org/10.1117/12.2047335
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

Digital copiers are now widely used. One major issue for a digital copier is copy quality. In order to achieve as high quality as possible for every input document, multiple processing pipelines are included in a digital copier. Every processing pipeline is designed specifically for a certain class of document, which may be text, picture, or a mixture of both as is illustrated by the three examples shown in Fig. 1. In this paper, we describe an algorithm that can effectively classify an input image into its corresponding category. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.

Citation Download Citation

Cheng Lu, Jerry Wagner, Brandi Pitta, David Larson, and Jan Allebach "SVM-based automatic scanned image classification with quick decision capability", Proc. SPIE 9015, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, 90150G (3 February 2014); https://doi.org/10.1117/12.2047335

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available