Graphical Models for Joint Segmentation and Recognition of License Plate Characters

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 graphic...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2009-01, Vol.16 (1), p.10-13
Main Authors: Fan, Xin, Fan, Guoliang
Format: Article
Language:English
Subjects:
Algorithms
Automatic license plate recognition
Belief propagation
Character recognition
character segmentation
Face recognition
Graphical models
Image recognition
Image segmentation
License plate recognition
Licenses
Markov random fields
Object detection
Optimization
Recognition
Segmentation
Speech recognition
State estimation
Tasks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2008.2008486