Context modeling for text/non-text separation in free-form online handwritten documents

Adrien Delaye; Cheng-Lin Liu

doi:10.1117/12.2001973

4 February 2013 Context modeling for text/non-text separation in free-form online handwritten documents

Adrien Delaye, Cheng-Lin Liu

Proceedings Volume 8658, Document Recognition and Retrieval XX; 86580C (2013) https://doi.org/10.1117/12.2001973
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Free-form online handwritten documents contain a high diversity of content, organized without constraints imposed to the user. The lack of prior knowledge about content and layout makes the modeling of contextual information of crucial importance for interpretation of such documents. In this work, we present a comprehensive investigation of the sources of contextual information that can benefit the task of discerning textual from non-textual strokes in handwritten online documents. An in-depth analysis of interactions between strokes is conducted through the design of various pairwise clique systems that are combined within a Conditional Random Field formulation of the stroke labeling problem. Our results demonstrate the benefits of combining complementary sources of context for improving the text/non-text recognition performance.

Citation Download Citation

Adrien Delaye and Cheng-Lin Liu "Context modeling for text/non-text separation in free-form online handwritten documents", Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580C (4 February 2013); https://doi.org/10.1117/12.2001973

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