… likelihood maximization to find the best parameters values of the license plate features and
its characters… fourth technique to segment the seven characters of the license plate using only …

… and transition probabilities, we decoded the highest probability character sequence …
selected using a grid search that maximized TPR performance for the no-LP class at 0.5% FPR. …

… of characters as a Markov random field (MRF). With the MRF modeling, the extraction of
characters is formulated as the problem of maximizing the a posteriori probability based on …

… In this paper, the license plate segmentation module extracts the license plate … the extraction
of characters is formulated as the problem of maximizing a posteriori probability based on a …

… SHMMs can be trained by the expectation maximization (EM) algorithm that has to be approxi…
We can use the similar maximum likelihood (ML) estimators as [9] for learning parameters …

… as an optimization problem of maximizing the a posteriori probability given prior knowledge …
2, we use the MRF model to formulate the character extraction problem as an optimization …

… modules for plate detection, character segmentation, and … they are clustered using the
Expectation–Maximization (EM) [… where p(xi) is the probability of the edge point xi, φ is the jth …

… a license plate region; 2) segmentation of the plate characters; and 3) recognition of each
character… can be modeled as the problem of maximizing a posteriori probability based on given …

… to segment the LP and finally to recognize all the characters in … Then it maximizes the contrast
to improve and clarify better … layer to predicate every class probabilities. Besides, we used …

… a license plate to infer characters and their positions using hidden Markov models. The
character sequence with the highest probability is … Transforms were selected to maximise class …