By Topic

Ink normalization and beautification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Simard, P.Y. ; Microsoft Res., One Microsoft Way, Redmond, WA, USA ; Steinkraus, D. ; Agrawala, M.

Handwriting recognition is difficult because of the high variability of handwriting and because of segmentation errors. We propose an approach that reduces this variability without requiring letter segmentation. We build an ink extrema classifier which labels local minima of ink as {bottom, baseline, other} and maxima as {midline, top, other}. Despite the high variability of ink, the classifier is 86% accurate (with 0% rejection). We use the classifier information to normalize the ink. This is done by applying a "rubber sheet" warping followed by a "rubber rod" warping. Both warpings are computed using conjugate gradient methods. We display the normalization results on a few examples. This paper illustrates the pitfalls of ink normalization and "beautification ", when solved independently of letter recognition.

Published in:

Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on

Date of Conference:

29 Aug.-1 Sept. 2005