License plate character segmentation using likelihood maximization

摘要

A method determines a license plate layout configuration. The method includes generating at least one model representing a license plate layout configuration. The generating includes segmenting training images each defining a license plate to extract characters and logos from the training images. The segmenting includes calculating values corresponding to parameters of the license plate and features of the characters and logos. The segmenting includes estimating a likelihood function specified by the features using the values. The likelihood function measures deviations between an observed plate and the model. The method includes storing a layout structure and the distributions for each of the at least one model. The method includes receiving as input an observed image including a plate region. The method includes segmenting the plate region and determining a license plate layout configuration of the observed plate by comparing the segmented plate region to the at least one model.

主权项

1. A method for determining a license plate layout configuration, the method comprising:
generating at least one model representing a license plate layout configuration, the generating including:
for training images each including a sample license plate, segmenting each character and logo from the at least one sample license plate into a bounding box representation,calculating values corresponding with each bounding box representation of the at least one sample license plate, andestimating a likelihood function for defining a distribution of the values storing a license plate layout configuration and the distribution of values for each of the at least one model; classifying an observed license plate in a captured image, the classifying including:
segmenting the observed license plate into segments using varying thresholds to extract segments, wherein each threshold applied to the observed license plate produces the segments making up a possible layout model; estimating features for the segments extracted for each possible layout model;
identifying candidate models by comparing measurements for the features against the distributions stored for the models;calculating a likelihood for each determined match between a layout structure of the each candidate model and the stored models, wherein the likelihood is calculated as a sum of a marginal (log) likelihood using the equation:
log p(x|s,m)=Σi log fi(x|s,m), where p(x|s, m) is the likelihood given the segmentation s under model m and observation x and fi(x|s, m) is marginal distributions for feature i under model m;
determining the model having the maximum likelihood; andassociating the observed license plate as having the license plate layout configuration associated with the model having the maximum likelihood.