By Topic

Unsupervised selection of HMMs architectures for handwritten text/word recognition

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

5 Author(s)
Hamdani, M. ; Res. Group on Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia ; Hamdani, T.M. ; Alimi, A.M. ; Abed, H.E.
more authors

The selection of the classifier architecture is a very important step in the recognition process. This paper presents a new algorithm for the HMMs architectures optimization: Multi-Models Evolvement using PSO (MME-PSO). The proposed algorithm is applied to an Arabic handwriting recognition system. The recognizer is based on character Hidden Markov Models which can have different architectures. This algorithm is evaluated using the IfN/ENIT database. The performance of the resulting system is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system (IfN-REGIM-2) performs an absolute improvement of 6% of word recognition rate with about 81% comparing to the baseline system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

Published in:

Computational Intelligence and Intelligent Informatics (ISCIII), 2011 5th International Symposium on

Date of Conference:

15-17 Sept. 2011