Abstract
Automatic Vehicle Identification (AVI) has many
applications in traffic systems (highway electronic toll collection, red
light violation enforcement, border and customs checkpoints, etc.).
License Plate Recognition is an effective form of AVI systems. In
this study, a smart and simple algorithm is presented for vehicle’s
license plate recognition system. The proposed algorithm consists of
three major parts: Extraction of plate region, segmentation of
characters and recognition of plate characters. For extracting the
plate region, edge detection algorithms and smearing algorithms are
used. In segmentation part, smearing algorithms, filtering and some
morphological algorithms are used. And finally statistical based
template matching is used for recognition of plate characters. The
performance of the proposed algorithm has been tested on real
images. Based on the experimental results, we noted that our
algorithm shows superior performance in car license plate
recognition.
Keywords—Character recognizer, license plate recognition, plate
region extraction, segmentation, smearing, template matching.
I. INTRODUCTION
UTOMATIC vehicle identification is an essential stage
in intelligent traffic systems. Nowadays vehicles play a
very big role in transportation. Also the use of vehicles has
been increasing because of population growth and human
needs in recent years. Therefore, control of vehicles is
becoming a big problem and much more difficult to solve.
Automatic vehicle identification systems are used for the
purpose of effective control.
License plate recognition (LPR) is a form of automatic
vehicle identification. It is an image processing technology
used to identify vehicles by only their license plates. Real time
LPR plays a major role in automatic monitoring of traffic
rules and maintaining law enforcement on public roads [1].
Since every vehicle carries a unique license plate, no external
cards, tags or transmitters need to be recognizable, only
license plate.
So many researches of car identification have been
approached by car license plate extracting and recognition,proposed automatic number-plate recognition
using optical character recognition techniques. Johnson and
Bird [3] proposed knowledge-guieded boundary following
and template matching for automatic vehicle identification.
Fahmy [4] proposed bidirectional associative memories
(BAM) neural network for number plate reading. It’s
appropriate for small numbers of patterns. Nijhuis, Ter
Brugge, Helmholf J.P.W. Pluim, L. Spaanenburg, R.S.
Venema and M.A.Westenberg [5] proposed fuzzy logic and
neural networks for car LPR. This method used fuzzy logic
for segmentation and discrete-time cellular neural networks
(DTCNN’S) for feature extraction. Choi [6] and Kim [7]
proposed the method based on vertical edge using Hough
transform (HT) for extracting the license plate. E.R. Lee, P.K.
Kim and H.J. Kim [8] used neural network for color extraction
and a template matching to recognize characters. S.K. Kim,
D.W. Kim and H.J. Kim [9] used a genetic algorithm based
segmentation to extract the plate region. Tavsanoglu and
Saatci [10] proposed an approach to form orientation map as
recognition feature using a Gabor filter for recognizing
characters. Yoshimura and Etoh [11] used Gabor jets
projection to form a feature vector for recognizing low
resolution gray-scale character. Hontani et.al. [12] proposed a
method for extracting characters without prior knowledge of
their position and size in the image. Park et. al. [13] devised a
method to extract Korean license plate depending on the color
of the plate. H.J. Kim, D.W. Kim, S.K. Kim, J.V. Lee, J.K.
Lee [14] proposed a method of extracting plate region based
on color image segmentation by distributed genetic.
In this study, the proposed algorithm is based on extraction
of plate region, segmentation of plate characters and
recognition of characters.
The paper is organized as follows: Section II provides an
overview of the overall system. Extracting the plate region is
explained in Section III. Section IV gives the segmentation of
individual plate characters. Section V deals with recognition
of characters based on statistical based template matching
algorithm. Section VI discusses experimental results and the
paper concludes with Section VII.

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