Learning shape features for document enhancement

Tayo Obafemi-Ajayi; Gady Agam; Ophir Frieder

doi:10.1117/12.838746

18 January 2010 Learning shape features for document enhancement

Tayo Obafemi-Ajayi, Gady Agam, Ophir Frieder

Author Affiliations +

Proceedings Volume 7534, Document Recognition and Retrieval XVII; 75340F (2010) https://doi.org/10.1117/12.838746
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

In previous work we showed that shape descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images. The algorithm encodes the pixel neighborhood information effectively using a variant of shape descriptor. However, the generation of the shape descriptor features was approached in a heuristic manner. In this work, we propose a system of learning the shape features from the training data set by using neural networks: Multilayer Perceptrons (MLP) for feature extraction. Given that the MLP maybe restricted by a limited dataset, we apply a feature selection algorithm to generalize, and thus improve, the feature set obtained from the MLP. We validate the effectiveness and efficiency of the proposed approach via experimental results.

Citation Download Citation

Tayo Obafemi-Ajayi, Gady Agam, and Ophir Frieder "Learning shape features for document enhancement", Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340F (18 January 2010); https://doi.org/10.1117/12.838746

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