University of Surrey

Test tubes in the lab Research in the ATI Dance Research

DBN-based structural learning and optimisation for automated handwritten character recognition

Pauplin, O and Jiang, J (2012) DBN-based structural learning and optimisation for automated handwritten character recognition Pattern Recognition Letters, 33 (6). pp. 685-692.

Full text not available from this repository.

Abstract

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. The classification performance greatly relies on the choice of a DBN model that will best describe the dependencies in each class of data. In this paper, we present DBN models trained for the classification of handwritten digit. Two approaches to improve the suitability of the models are presented. One uses a fixed DBN structure, and is based on an Evolutionary Algorithm optimisation of the selection and of the layout of the observations for each class of data. The second approach is about learning part of the structure of the models from the training set of each class. Parameter learning is then performed for each DBN. Classification results are presented for the described models, and compared with previously published results. Both approaches were found to improve the recognition rate compared to previous results.

Item Type: Article
Authors :
NameEmailORCID
Pauplin, OUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 2012
Identification Number : 10.1016/j.patrec.2011.12.010
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:22
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835069

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800