Ms. Shravya R. Shetty Profile

Shravya R. Shetty

at Univ at Buffalo

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 29 January 2007 Paper

Segmentation and labeling of documents using conditional random fields

Shravya Shetty, Harish Srinivasan, Matthew Beal, Sargur Srihari

Proceedings Volume 6500, 65000U (2007) https://doi.org/10.1117/12.704410

KEYWORDS: Image segmentation, Data modeling, Nickel, Neural networks, Feature extraction, Image retrieval, Performance modeling, Lithium, Image processing algorithms and systems, Neurons

Read Abstract +

The paper describes the use of Conditional Random Fields(CRF) utilizing contextual information in automatically labeling extracted segments of scanned documents as Machine-print, Handwriting and Noise. The result of such a labeling can serve as an indexing step for a context-based image retrieval system or a bio-metric signature verification system. A simple region growing algorithm is first used to segment the document into a number of patches. A label for each such segmented patch is inferred using a CRF model. The model is flexible enough to include signatures as a type of handwriting and isolate it from machine-print and noise. The robustness of the model is due to the inherent nature of modeling neighboring spatial dependencies in the labels as well as the observed data using CRF. Maximum pseudo-likelihood estimates for the parameters of the CRF model are learnt using conjugate gradient descent. Inference of labels is done by computing the probability of the labels under the model with Gibbs sampling. Experimental results show that this approach provides for 95.75% of the data being assigned correct labels. The CRF based model is shown to be superior to Neural Networks and Naive Bayes.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years