6. The method according to claim 1 , wherein the segmenting the plate region includes: (a) producing a binary map of the plate region; (b) determining object components relative to background components; (c) performing a projection in a vertical region; (d) locating gaps between components by applying a threshold for valley detection; (e) dividing the region into a sub-region at the gap; (f) determining a collection of black pixels in each sub-region as including a candidate for a character or logo; (g) matching the collections of black pixels against the at least one model; (h) determining if a matching score for the model is below a pre-set threshold; and (i) determining a model that is a closest match to the plate region in the observed image.
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, and estimating 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 )=Σ i log f i ( x|s,m ), where p(x|s, m) is the likelihood given the segmentation s under model m and observation x and f i (x|s, m) is marginal distributions for feature i under model m; determining the model having the maximum likelihood; and associating the observed license plate as having the license plate layout configuration associated with the model having the maximum likelihood.