19-10-2012, 03:04 PM
EXTRACTION AND RECOGNITION OF THE VEHICLE LICENSE PLATE
FOR PASSING UNDER OUTSIDE ENVIRONMENT
[attachment=36692]
Abstract
Persian License Plate Detection and Recognition
System is an image-processing technique used to identify a
vehicle by its license plate. In fact this system is one kind of
automatic inspection of transport, traffic and security systems
and is of considerable interest because of its potential
applications to areas such as automatic toll collection, traffic
law enforcement and security control of restricted areas.
License plate location is an important stage in vehicle license
plate recognition for automated transport system. This paper
presents a real time and robust method of license plate
detection and recognition from cluttered images based on the
morphology and template matching. In this system main stage
is the isolation of the license plate from the digital image of the
car obtained by a digital camera under different circumstances
such as illumination, slop, distance, and angle. The algorithm
starts with preprocessing and signal conditioning. Next license
plate is localized using morphological operators. Then a
template matching scheme will be used to recognize the digits
and characters within the plate. This system implemented with
help of Isfahan Control Traffic organization and the
performance was 98.2% of correct plates identification and
localization and 92% of correct recognized characters. The
results regarding the complexity of the problem and diversity
of the test cases show the high accuracy and robustness of the
proposed method. The method could also be applicable for
other applications in the transport information systems, where
automatic recognition of registration plates, shields, signs, and
so on is often necessary. This paper presents a morphologybased
method.
INTRODUCTION
While the first industrial automatic system for Car
License Plate Recognition (LPR) was introduced in the
80’s, an outburst of commercial systems occurred in the 90s.
Although that a lot of LPR systems are available in the
market, the research and development still continues and
new sophisticated solutions to plate localization, character
segmentation and recognition appear.
This is due to the growing demand for the automatic
vehicle identification required for traffic control, border
control, access- control, calculation of parking time and
payment, search for stolen cars or unpaid fees, and the
requirement for reliable identification at different lighting
conditions, presence of random or structured noise in the
plate, and nationality specific features, concerning plate’s
size and type of characters [1, 2]. Figure 1 illustrates a
typical license plate recognition system.
Character Recognition using Template matching
Before employment of the recognition algorithm, the
characters would be normalized. Normalization is to refine
the characters into a block containing no extra white spaces
(pixels) in all the borders of the characters. Then each
character is fit to a 38x20 block as shown in Figure 5.
Fitting approach is also necessary for template matching.
For matching the characters with the database, input images
must be equalizing to a 38 x 20 block with the database
characters. In this system the characters are fit to 38x20.
The extracted characters cut from plate and the characters
on database are now equalized. The next step is template
matching. Template matching is an effective algorithm for
recognition of characters. The characters’ image is
compared to the ones in the database and the best similarity
is considered. To measure the similarity and find the best
match, a statistical method correlation based is used.
Correlation is an effective technique for image recognition.
This method measures the correlation coefficient between a
number of known images with the same size unknown
images or parts of an image with the highest correlation
coefficient between the images producing the best match.
This system used the database as the Iranian license plates
characters all 25 alphanumeric characters (16 alphabets and
9 numerals) with the size of 38x20.
CONCLUSION
In this paper, we presented a novel method of identifying
and recognizing of Iranian car license plates. Firstly we
extracted the plate location, and then we separated the plate
characters by segmentation and applied a correlation based
template matching scheme for recognition of plate
characters. This system is customized for the identification
of Iranian license plates. The system is tested over a large
number (more than 150) of images, where this algorithm
performs well on different types of vehicles including
Iranian car and motorcycle plates as well as diverse
circumstances. Finally it is proved to be %97.3 correct in
the extraction of plate region and %94 correct in the
segmentation of the characters and %92 in the recognition
of the characters. We believe that this system can be
redesigned and tested for multinational car license plates in
the future time regarding their own attributes.
FOR PASSING UNDER OUTSIDE ENVIRONMENT
[attachment=36692]
Abstract
Persian License Plate Detection and Recognition
System is an image-processing technique used to identify a
vehicle by its license plate. In fact this system is one kind of
automatic inspection of transport, traffic and security systems
and is of considerable interest because of its potential
applications to areas such as automatic toll collection, traffic
law enforcement and security control of restricted areas.
License plate location is an important stage in vehicle license
plate recognition for automated transport system. This paper
presents a real time and robust method of license plate
detection and recognition from cluttered images based on the
morphology and template matching. In this system main stage
is the isolation of the license plate from the digital image of the
car obtained by a digital camera under different circumstances
such as illumination, slop, distance, and angle. The algorithm
starts with preprocessing and signal conditioning. Next license
plate is localized using morphological operators. Then a
template matching scheme will be used to recognize the digits
and characters within the plate. This system implemented with
help of Isfahan Control Traffic organization and the
performance was 98.2% of correct plates identification and
localization and 92% of correct recognized characters. The
results regarding the complexity of the problem and diversity
of the test cases show the high accuracy and robustness of the
proposed method. The method could also be applicable for
other applications in the transport information systems, where
automatic recognition of registration plates, shields, signs, and
so on is often necessary. This paper presents a morphologybased
method.
INTRODUCTION
While the first industrial automatic system for Car
License Plate Recognition (LPR) was introduced in the
80’s, an outburst of commercial systems occurred in the 90s.
Although that a lot of LPR systems are available in the
market, the research and development still continues and
new sophisticated solutions to plate localization, character
segmentation and recognition appear.
This is due to the growing demand for the automatic
vehicle identification required for traffic control, border
control, access- control, calculation of parking time and
payment, search for stolen cars or unpaid fees, and the
requirement for reliable identification at different lighting
conditions, presence of random or structured noise in the
plate, and nationality specific features, concerning plate’s
size and type of characters [1, 2]. Figure 1 illustrates a
typical license plate recognition system.
Character Recognition using Template matching
Before employment of the recognition algorithm, the
characters would be normalized. Normalization is to refine
the characters into a block containing no extra white spaces
(pixels) in all the borders of the characters. Then each
character is fit to a 38x20 block as shown in Figure 5.
Fitting approach is also necessary for template matching.
For matching the characters with the database, input images
must be equalizing to a 38 x 20 block with the database
characters. In this system the characters are fit to 38x20.
The extracted characters cut from plate and the characters
on database are now equalized. The next step is template
matching. Template matching is an effective algorithm for
recognition of characters. The characters’ image is
compared to the ones in the database and the best similarity
is considered. To measure the similarity and find the best
match, a statistical method correlation based is used.
Correlation is an effective technique for image recognition.
This method measures the correlation coefficient between a
number of known images with the same size unknown
images or parts of an image with the highest correlation
coefficient between the images producing the best match.
This system used the database as the Iranian license plates
characters all 25 alphanumeric characters (16 alphabets and
9 numerals) with the size of 38x20.
CONCLUSION
In this paper, we presented a novel method of identifying
and recognizing of Iranian car license plates. Firstly we
extracted the plate location, and then we separated the plate
characters by segmentation and applied a correlation based
template matching scheme for recognition of plate
characters. This system is customized for the identification
of Iranian license plates. The system is tested over a large
number (more than 150) of images, where this algorithm
performs well on different types of vehicles including
Iranian car and motorcycle plates as well as diverse
circumstances. Finally it is proved to be %97.3 correct in
the extraction of plate region and %94 correct in the
segmentation of the characters and %92 in the recognition
of the characters. We believe that this system can be
redesigned and tested for multinational car license plates in
the future time regarding their own attributes.