THE 3 rd INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
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of the license plate is the same with the color of the car. Therefore, the best
choice for the localization of the license plate is the connected components
method (mathematical morphology). That’s why, we have used the
connected components algorithm in the software. The connected components
labeling operator scans the image by moving along a row until it comes to a
point
p (where
p denotes the pixel to be labeled at any stage in the scanning
process) for which
V={1} . When this is true, it examines the four neighbors
of
p which have already been encountered in the scan. Based on this
information, the labeling of
p occurs as follows:
If all four neighbors are 0, assign a new label to
p , else
if only one neighbor has
V={1} , assign its label to
p , else
if more than one of the neighbors have
V={1} , assign one of the
labels to
p and make a note of the equivalences.
Character segmentation is an operation that seeks to decompose an
image of a sequence of characters into sub-images of individual symbols.
There are three main segmentation techniques such as Explicit Segmentation
(Pure Segmentation), Implicit Segmentation (Recognition Based
Segmentation) and Holistic (Segmentation Free). In Explicit Segmentation,
the input image is divided into sub-images of characters which are classified
lately. However, in Implicit Segmentation, the system searches the image for
components that match classes in its alphabet. The last technique recognizes
an entire word as a unit which brings a drawback. Since this technique do
not deal directly with letters but only with words, recognition is necessarily
constrained to a specific lexicon of words.
References [1] S.K.a.H.F., “Connected Component Labeling Algorithm for very complex and high
resolution images on an FPGA platform,” in
German Aerospace Center and German Space Operations Center , Wessling.
[2] B.M. &. V. B., “Analysis of Segmentation Performance on the CEDAR Benchmark
Database,” in
6th International Conference on Document Analysis and Recognition ,
Seattle, 2001.
[3] G.R and W. R, “Digital Image Processing,” in
Addison-Wesley Publishing Company , New
York, 1992.