Graphical Models for Joint Segmentation and Recognition of License Plate Characters

Author

Fan, Xin ; Fan, Guoliang

Volume

16

Issue

1

fYear

2009

Firstpage

10

Lastpage

13

Abstract

We formulate the issue of joint image segmentation and recognition as an integrated statistical inference problem. A two-layer graphical model is proposed that supports the optimal segmentation and recognition in an unified Bayesian framework. Due to the explicit modeling of two tasks in the graphical model, an efficient non-iterative belief propagation algorithm is used for state estimation. The proposed approach is applied to automatic licence plate recognition (ALPR), and it outperforms traditional methods where the two tasks are implemented independently and sequentially.

Keywords

Bayes methods; graph theory; image segmentation; optical character recognition; statistical analysis; traffic engineering computing; automatic license plate character recognition; integrated statistical inference problem; joint image segmentation; noniterative belief propagation algorithm; state estimation; two-layer graphical model; unified Bayesian framework; Belief propagation; Character recognition; Face recognition; Graphical models; Image recognition; Image segmentation; Licenses; Markov random fields; Object detection; Speech recognition; Automatic license plate recognition; belief propagation; character segmentation; graphical models;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2008.2008486

Filename

4711337