Mr. Bart Dolega Profile

Bart Dolega

at Illinois Institute of Technology

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 | 28 January 2008 Paper

Stroke frequency descriptors for handwriting-based writer identification

Bart Dolega, Gady Agam, Shlomo Argamon

Proceedings Volume 6815, 68150I (2008) https://doi.org/10.1117/12.767227

KEYWORDS: Image segmentation, Feature extraction, Databases, Image retrieval, Image processing algorithms and systems, Image filtering, Principal component analysis, System identification, Analytical research, Image analysis

Read Abstract +

Writer identification in offline handwritten documents is a difficult task with multiple applications such as authentication, identification, and clustering in document collections. For example, in the context of content-based document image retrieval, given a document with handwritten annotations it is possible to determine whether the comments were added by a specific individual and find other documents annotated by the same person. In contrast to online writer identification in which temporal stroke information is available, such information is not readily available in offline writer identification. The base approach and the main contribution of our work is the idea of using derived canonical stroke frequency descriptors from handwritten text to identify writers. We show that a relatively small set of canonical strokes can be successfully employed for generating discriminative frequency descriptors. Moreover, we show that by using frequency descriptors alone it is possible to perform writer identification with success rate which is comparable to the known state of the art in offline writer identification with close to 90% accuracy. As frequency descriptors are independent of existing descriptors, the performance of offline writer identification may be improved by combining both standard and frequency descriptors. Experimental evaluation with quantitative performance evaluation is provided using the IAM dataset.¹

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