Boosting based text and non-text region classification

Binqing Xie; Gady Agam

doi:10.1117/12.876736

24 January 2011 Boosting based text and non-text region classification

Binqing Xie, Gady Agam

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 787416 (2011) https://doi.org/10.1117/12.876736
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Layout analysis is a crucial process for document image understanding and information retrieval. Document layout analysis depends on page segmentation and block classification. This paper describes an algorithm for extracting blocks from document images and a boosting based method to classify those blocks as machine printed text or not. The feature vector which is fed into the boosting classifier consists of a four direction run-length histogram, and connected components features in both background and foreground. Using a combination of features through a boosting classifier, we obtain an accuracy of 99.5% on our test collection.

Citation Download Citation

Binqing Xie and Gady Agam "Boosting based text and non-text region classification", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 787416 (24 January 2011); https://doi.org/10.1117/12.876736

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