By Topic

Automatic training of page segmentation algorithms: an optimization approach

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
$33 $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

2 Author(s)
Song Mao ; Center for Autom. Res., Maryland Univ., College Park, MD, USA ; T. Kanungo

Most page segmentation algorithms have user-specifiable free parameters. However, algorithm designers typically do not provide a quantitative/rigorous method for choosing values for these parameters. The free parameter values can affect the segmentation result quite drastically and are very dependent on the particular dataset that the algorithm is being used on. We present an automatic training method for choosing free parameters of page segmentation algorithms. The automatic training problem is posed as a multivariate non-smooth function optimization problem. An efficient direct search method-simplex method-is used to solve this optimization problem. This training method is used applied to the training of Kise's page segmentation algorithm. It is found that a set of optimal parameter values and their corresponding performance index can be found using relatively few function evaluations. The UW III dataset was used for conducting our experiments

Published in:

Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:4 )

Date of Conference: