Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Letter-level shape description by skeletonization in faded documents

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)
Singh, R. ; Dept. of Comput. Sci. & Eng., Minnesota Univ., Minneapolis, MN, USA ; Wade, M.C. ; Papanikolopoulos, N.P.

We present a method for determining the skeletal shape description for letters in texts faded due to ageing and/or poor ink quality. The proposed algorithm is interesting in that it neither involves assumptions about demarcation of object regions from the background, nor does it require pixel connectivity in the text regions. Consequently, it may be applied for obtaining the shape descriptions of “sparse” regions, which are characteristic of letters in faded documents. Given the pixel distribution for a letter or a word from a faded document, the method involves an iterative evolution of a piecewise-linear approximation of the principal curve of this pixel distribution. By constraining the principal curve to lie on the edges of the Delaunay triangulation of the shape distribution, the adjacency relationships between regions in the shape can be detected and used in evolving the skeleton. The approximation of the principal curve, on convergence, gives the final skeletal shape. The skeletonization is invariant to Euclidean transformations and is adaptive in terms of the topology of the underlying shape distribution as well as in the number of units needed for the piece-wise approximation of the principal curve

Published in:

Applications of Computer Vision, 1998. WACV '98. Proceedings., Fourth IEEE Workshop on

Date of Conference:

19-21 Oct 1998