The document discusses various algorithms for license plate character segmentation that are used in license plate recognition systems. It describes 12 different character segmentation algorithms: blob extraction, connected component analysis, projection-based combination methods, line scanning, rule-based segment analysis, image scissoring, morphological operations, prior knowledge-based, and block-based thresholding methods. The document provides details on the process, advantages, and disadvantages of each algorithm and compares their accuracy rates, with most methods achieving over 90% accuracy. The purpose is to categorize and summarize these different character segmentation algorithms.

1. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
VEHICLE LICENSE PLATE CHARACTER
SEGMENTATION – A STUDY
V.Karthikeyan1, R.Sindhu2, K.Anusha3, D.S.Vijith4
1
Assistant Professor, SVS College of Engineering
2,3,4
UG Students, SVS College of Engineering,
Tamilnadu, India
1
karthick77keyan@gmail.com, 2sindhus1312@gmail.com, 3anusharaghu44@gmail.com,
Abstract- License plate recognition (LPR) plays a major
role in this busy world, as the number of vehicles increases
day by day, theft of vehicles, breaking traffic rules, entering
restricted area are also increases linearly, so to block this act
license plate recognition system is designed. License Plate
Recognition (LPR) systems basically consist of 3 main
processing steps such as: Detection of number plate,
Segmentation of plate characters and Recognition of each
character. Among this, character segmentation is a most
challenging task, as the accuracy of the character
recognition relies on the accuracy of the character
segmentation. Problems of different lighting condition,
adhesion, fracture, rivet, rotation degrades the accuracy of
the character segmentation. So in order to overcome these
problems and uplift the accuracy of character segmentation
various algorithms are developed for this work. Purpose of
this paper is to categorize and brief them.
Keywords - License plate, License Plate Recognition
(LPR), Preprocessing, Character Segmentation.
1. INTRODUCTION
LPR (License Plate Recognition) is an imageprocessing technology used to identify vehicles by
their license plates. The Vehicle Identification
Number (VIN) is a unique identification number for
every car manufactured. Unlike registration
number, license plate number does not change
throughout the lifetime. Moreover, the registration
number is not unique to a car since it can be carried
forward to another car. Since the 21st century, with
social development and improvement of living
standards, the number of vehicles is continuously
increased, the traffic conditions is worsening, which
brought huge pressures to the society and
environment. License plate recognition system can
solve the various road problems generated by the
traffic congestion, thus receiving more and more
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4
vijeepsvs@gmail.com,
attention. The vehicle license plate recognition
system focuses on the key technologies, which
include the license plate region location, license
plate character extraction and license character
segmentation, license plate character recognition.
For different countries the type of license plate,
characters used in the plate, climatic conditions vary
so to cope with this we should establish a good
license plate recognition system with higher
performance and accuracy rate. But the recognition
step mainly relies on the accuracy rate of the
character segmentation. Thus we should strengthen
the character segmentation step with more powerful
and efficient algorithm. One algorithm could work
very well for a given country’s plate but very poorly
for another. So based upon our requirement we
should select the algorithms. This paper covers
various character segmentation algorithms; also the
process, advantages, and disadvantages of those
algorithms are discussed in this paper. Various
algorithms discussed in this paper are character
segmentation based on horizontal projection,
vertical projection, prior knowledge of the license
plate, inherent characteristics of the character, rule
based segment analysis, vector quantization,
connected component analysis (CCA), blob
extraction, morphological operations, filtering,
smearing algorithm, line scanning method, image
scissoring algorithm, template matching and Hough
transform.
All these methods are discussed in this paper in the
following section. And the comparison of the
accuracy/performance rates of these algorithms also
presented in this paper.
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2. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
2. VARIOUS METHODS FOR CHARACTER
SEGMENTATION
A.Blob Extraction based Character Segmentation.
In [12], lack of algorithm description, estimation
and result analysis of vertical projection and
connected component analysis (CCA) overcome by
this algorithm. Also the previous methods are not
sufficient to handle the low quality images but this
proposed algorithm adopting with the previous
methods like CCA, vertical projection and
binarization methods. This character segmentation
procedure consists of three sub parts. Adjusting the
input image and generating a binary image is the
first part. In this part Otsu’s method not working
well and Niblack’s method shows better result and
performance than Sauvola’s method so Niblack’s
method is used as the binarization algorithm.
Analyzing blobs which is a connected set of pixels
in the binary image, and removing the noisy blobs
and also merging and splitting the blobs is the
second part. In this step it starts with CCA, the noncharacter blobs, Plate boundaries, small dirties, and
unrelated marks or bars are excluded. Correcting the
rotated plate images and selecting final seven
character blobs is the last part. In this module seven
blobs with higher matching scores are selected.
When analyzing the failure cases closely this robust
character segmentation module achieves 97.2% of
success rate. But the drawback of this method is
that naturally blob analysis failed in some cases.
But here a single parameter for Niblack’s
binarization method is present. But the parameter is
fine for the general images, but it is not the best one
for each image.
Figure 1: Results of the character segmentation
steps.
©http://ijcer.org
B. Character segmentation using connected
component analysis (CCA).
In [6], based on pixel connectivity the CCA
scans and labels the pixels of a binarized image into
components. Every pixel is labeled with a value
depending on the component to which it was
assigned. The connected components are then
analyzed to filter out long and wide components and
only left the components based on the defined
values. But the drawback [], is that in this method
the segmented result using may not contain the
exact license plate regions.
Figure 2: 4-neighbourhood and 8-neighbourhood
Figure 3: Car images segmented license plates on
right
C. Character segmentation based on the
combination of projection
In [11], preprocessing is strengthened before
segmentation stage to improve the accuracy of the
character segmentation. Projection based method to
segment the characters according to the prior
knowledge of the license plate. It consists of two
steps: coarse segmentation and fine segmentation.
In coarse segmentation vertical projection is passed
to the preprocessed plate, several blocks with
projection nonzero were obtained. They may be
plate characters or noises, they are candidate
characters i.e., plate character or noises. Record
horizontal start and end locations. Then horizontal
projection is applied to each candidate character and
record vertical start and end locations. At the end,
every candidate character can get a common
bounding box. Fine segmentation gives solutions to
3 major problems. Problems and solutions are
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3. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
(i)Coarse segmentation is not ideal so boundaries
need adjustment. (ii)For broken characters character
merging and splitting must done (iii)The vertical
frames may connect with first and last character so
it is necessary to separate the plate character and
noises. After locating plate, character segmentation
will directly affect the quality of character
recognition accuracy so this method solves this
problem and provides better segmentation. By the
combined action of projection and character’s
inherent characteristics for tilted characters in
horizontal direction there is no need for tilt
correction but we can achieve better segmentation.
This method solve character bond and break
problem and are not sensitive to inclination. So by
using this method we can achieve 97% accuracy.
But the drawback is that error usually occurs in
fuzzy image which is very hard to separate the
character from the background.
(a) Binary plate image
(b) Preprocessing
size normalization (plate images are normalized to
160*40 in pixel), determination of plate kind and
object enhancement (to improve the quality of
images). Then in Horizontal segmentation Hough
transformation is used to detect lines. It is hard to
obtain the horizontal segment lines for large
segmentation by horizontal projection analysis. So
Hough transform is preferred. This Hough
transformation is used on the midpoints of all
subsection lines to remove the wrong subsection
lines and combine the correct subsection lines into a
whole line. Also the horizontal segment lines of the
block with bolt are frequently wrong and can be
removed by Hough transformation. This Hough
transformation can solve the problem of rivet,
rotation, and lighting inconsistency. And then
projection based vertical segmentation algorithm is
applied using prior knowledge. Thus the use of
prior knowledge leads to accurate segmentation and
hold back the power of plate frame and space mark.
Also segmentation is more accurate and strong than
single projection method because of Hough
transformation and the prior knowledge. But these
methods can't work with some other kind of license
plate, like two-row license plate.
E. Character segmentation using line scanning
method
(c) Coarse segmentation
(d) Fine segmentation
Figure 4: Character segmentation
D. Character segmentation using Hough
transformation
In [13], presents a fresh algorithm for character
segmentation. This new algorithm uses Hough
transformation and the prior knowledge in
horizontal and vertical segmentation to overcome
the difficulties like image noise, problems in plate
frame, rivet, space mark, plate rotation and lighting
inconsistency. This method uses information of
intensity and avoids the absorption so it overcomes
the drawbacks of binarization. The algorithm has
three steps: preprocessing, horizontal segmentation
and vertical segmentation. For better performance
preprocessing is must. Preprocessing step consist of
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In [1], effective character segmentation is
performed using line scanning technique. Usually
scanning done from left to right of the license
plate.The process involves several steps, first the
gray scale image is converted into binary image. In
order to partition the text on the license plate into
lines ‘Lines’ function which uses “clip” function is
used. Usually this “Clip” function crops black letter
with white background. And then resizing is done.
At the end same procedure is repeated on the
cropped image till all the characters are segmented.
Figure 5: License Plate Character Segmentation
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4. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
F. Character segmentation using rule based
segment analysis engine.
In [8], a segment analysis engine is used to find
out the corresponding license plate regions from the
edge map images. This work is passed out more
than two metro cities in India to produce 2500
ground-truth images. The motivation behind this
work is to uplift the accuracy and the efficiency of
license plate localization, to overcome the
degradation and failure of result due to polluted
environment and huge variation in the features of
the license plate like different size, color and font of
the license plate and to generate a better method to
segment the characters of the license plate. For
localizing purpose 8-connected edge components
are segmented and labeled exclusively using a
connected component labeling (CCL) algorithm.
But the segmented result using CCL algorithm may
not contain the exact license plate regions. So, a
rule based segment analysis engine is developed for
successful collection of potential license plate
regions from the segmented results, using the
features: area of the segment (to identify the valid
segments), aspect ratio of the segment (to discard
the noise segment), Vertical edge gradient of the
segment (segments under this step are considered as
the potential license plate region). This proposed
work seems to have a better accuracy in its output
and efficiently segment the characters from the
license plate. But the drawback of this technique is
however well-known vertical edge components
visible in some non-plate regions of the images.
G. Image scissoring algorithm based character
segmentation
In [7], image scissoring algorithm is used to
identify the license plate characters, license plate is
divided into several images that each contain one
isolated character Several steps involves in this
segmentation process, first image contrast is done.
The Otsu method is used to perform thresholding of
plate image then connected components are
searched and labeled and the as per the standard
height and width all the characters are resized.
Since license plates do not have a fixed number of
characters for all plates, vertical projection and
connected- component method does not segment the
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license plate, so image scissoring algorithm is used
in this paper. Using this method the performance of
character segmentation reaches 95%.
Figure 6: Identified Character Components
H .Morphological and partition based character
segmentation.
In [10], stated character segmentation job is very
difficult due to several factors like image noise,
plate frame, rivet, and rotation and lighting
variance. So in order to get a good performance of
character segmentation preprocessing stage is
significant. Initially, image is filtered and noises are
removed. During the threshold processing several
small objects that directly affect the segmentation
process may grow on the threshold image due to the
problems of various lighting conditions, low quality
camera and motion effect. So a morphological
process which looks for the whole image for small
connected elements and remove it. Then to separate
the characters that are close with each other,
dilation operator is applied to the image. Following
this stage, in order to extract the character from the
plate partition scanning is projected. In this partition
method based on partition value character and
background are separated. After conducting several
experiments, a Partition value larger than 0.7-0.8 is
measured as background, if not it is measured as
character. At last the plate is divided into two
blocks with digits in first block and letter in the
second block.
The input color image with the size of 640x480.
And tested the images under a variety of lighting
condition and distance. As per this algorithm
performs well on different types of vehicles
including Iranian car and motorcycle plates as well
as miscellaneous conditions. By this method
character segmentation achieves accuracy of 94%.
But hardly any images of tremendously poor quality
attempted more than three probable strips.
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5. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
Figure 7: Segmentation
I. Prior knowledge based character segmentation.
In [5], for character segmentation prior
knowledge like size of the license plate, size of a
character, size of the interval between the characters
and the number of characters within a license plate
is used.
As we have the prior knowledge of the license plate,
if there is only one type of license plate this method
will be very effective. But projection based
segmentation is the traditional method that is used
in majority of the system. Horizontal and vertical
projections are used to perform horizontal and
vertical segmentation respectively. Horizontal
segmentation is used to remove the unwanted top
and bottom parts of the license plate image. And
vertical segmentation is used to separate the
characters that are in the same license plate image.
But when projection method is used alone it will
leads to problems like rivet, rotation and
illumination variance. When these problems appear
it will leads to several errors in the character
segmentation. So prior knowledge about the license
plate is used along with projection method in order
to eliminate the problems when projection method
is used alone and helps in deleting the wrong
horizontal segmentation and combining the correct
lines So character segmentation using prior
knowledge of license plate simple and fast.
normal case, for binarization fixed threshold is used
so it is difficult to separate the characters during
random lighting conditions and a dirt license plate.
Thus for block-based thresholding, an extracted LP
of size PxQ into non-overlapped blocks of size
MxN, where M=P/4 and N=Q/4. But even some
specific characters like ‘F’ are not binarized it is
still sufficient for the extraction of top and bottom
boundaries of characters for further processing.
After binarization , the top and bottom boundaries
of the characters are confirmed, and the horizontal
projection is carried out. But even some other least
projection points which may hinder the detection
accuracy. In order to manage with this, the
searching strategy is given with, the imaginary
height of the License plate and vertical coordinate
of License plate. Initially the bottom boundaries of
License plate should locate within the vertical
region and then the top boundaries of License plate
should locate within the vertical region. At the end
the boundary of every character can be resolute by
the horizontal and vertical projections. Based on the
extracted boundaries, all the characters are cropped
from the original grayscale basis and then
normalized to 10x20. Then again the cropped
grayscale basis will be binarized with its mean.
Then from the binarized output the object with
utmost area size will pass through connected
component processing. This projection based
method achieves good accuracy. That the accurate
rates of the character segmentation by this method
can achieve 95.2
Figure 8: License plate binarization in various
situations
J. Projection Based character segmentation
In [5], a projection based method and binarization
is used for character segmentation. In order to
improve the segmentation rate this method
considered the relationship among characters. In
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6. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
Figure 9: Horizontal projection results
Figure 10: Vertical projection results
K. Smearing algorithms, filtering and
morphological algorithms based character
segmentation
In [9], smearing algorithms, filtering and some
morphological algorithms are used for character
segmentation. License plate is segmented into its
ingredient parts and the characters are obtained
individually in the segmentation process. This
segmentation process consists of four steps.
Filtering the image for enhancement and removing
noises and unrelated parts are the first step. Then
separating the characters that are close with each
other by dilation operation is the second step.
Finding the character regions by horizontal and
vertical smearing is the third step. Cutting the plate
characters is the final step of this process.
combines the projection and template match, but
also improves the techniques. In order to overcome
the problems of different light strength in different
time, partial light shadow in the image and adhesive
characters new method is used. Projection method is
done in the horizontal direction to detect the edge of
the single license plate in the vertical direction.
Refined character segmentation is done using
template matching as templates designed in the
literature of other works are difficult to meet the
images that are shot random. These templates are
designed on the basis of prior knowledge about the
license plate, and take similarity measures to match
the image segmented by projection. So by this
method accuracy requirements are satisfied along
with proper segmentation. Image normalization is
necessary due to the usage of template matching
method. Then to improve the accuracy of image
segmentation minimum distance classifier is used.
As single method is very difficult to perform the
task so a combination of algorithms. Thorny
floating-point operations are restricted by the
template matching method. It is only the use of
arithmetical operations. The overall algorithm
reduces the complexity, also has great robust. The
algorithm is simple and effective. As per this
method, overall the segmentation rate is 98.8% and
time for performing the segmentation is about
10ms.
L.Vector quantization based character
segmentation
Figure 11: Locations of plate characters
This algorithm shows greater performance.
Character segmentation method achieves accuracy
of about 96%.
Figure 12: Individual characters
L. Template Matching and projection based
character segmentation.
In [2], for license plate character segmentation a
new method has been proposed. The originality lies
in this case in its treatment which not only
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In [3], presents a fresh algorithm for license
plate character segmentation problems by using
local vector quantization. To extract the correct
boundary and segment the plate region the binary
split tree is used for vector quantization which
becomes ready for the optical character recognition.
The output of the system is the bounding box for the
license plates detected and their segmented
characters. Gabor filter performs well at detection,
but its segmentation performance is poor. So
nonlinear vector quantization is applied to remove
the false alarms and to segment the license plate
characters to its accurate boundary Vector
quantization is a process of allocating pixel values
in one of a finite number of vectors. These vectors
are determined in such a way using binary split tree
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7. V. Karthikeyan, et al International Journal of Computer and Electronics Research [Volume 2, Issue 1, February 2013]
method so such that quantization error is
minimized. And at the end, connected component
analysis is applied to the quantized image to gain
the character segments. This method is effective
segmenting the plate characters. In this method the
performance rate of this License plate segmentation
is 94.2%. But the drawback of this method is
computationally very expensive.
Figure 13: segmentation results of various sizes and
forms of license plate
3. PERFORMANCE MEASURES OF LICENSE
PLATE CHARACTER SEGMENTATION
TECHNIQUES
Table. 1: Percentage of Performance measures
S.No
Algorithms used for
character segmentation
1
2
Blob Extraction [12]
Combination of projection
and inherent characteristics
of the character [11]
Image scissoring
algorithm[7]
Morphological and
partition based method[10]
Projection based method[5]
3
4
5
6
7
8
Smearing algorithm,
filtering and morphological
algorithm[9]
Template matching and
projection based method[2]
Vector quantization[3]
©http://ijcer.org
Percentage of
performance/
accuracy (%)
97.2
97
4. CONCLUSION
This paper presents various algorithms for
license plate character segmentation. License Plate
Recognition (LPR) system plays a vital role in
Intelligent Transportation System (ITS). It controls
various problems like traffic congestion, and used in
various applications like border crossing, law
enforcement, parking, automatic toll gates etc., this
LPR system comes across with various problems
like lighting condition, rivet, various size, form,
break in the plate, so in order to produce an
effective result the recognition rate must be good.
So, as we know recognition rate mainly rely on the
accuracy rate of the character segmentation. If
segmentation part fails or the output of character
segmentation is not fair then it will have some
effect on the recognition output. Thus to generate a
valuable output we presented various algorithms for
character segmentation. Among all the above
methods projection based method is the traditional
method used in various systems, but this method is
not much effective alone. So if the characteristic of
the license plate and prior knowledge about the
license plate is used along with projection method
we can get a better accuracy and effective result.
Character segmentation accuracy can also improved
by strengthening the preprocessing before
segmentation process. Algorithms presented in this
paper have both positive and negative sides. Certain
algorithm works well in one country but not for
other countries, so based upon our requirement we
should select the algorithm for our system.
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