29-10-2010, 06:49 PM
21-05-2011, 02:41 PM
hi friend you can refer these pages to get the details on NUMBER PLATE RECOGNITION
https://seminarproject.net/Thread-automa...ecognition
https://seminarproject.net/Thread-automa...tion--1460
https://seminarproject.net/Thread-automa...ecognition
https://seminarproject.net/Thread-automa...tion--1460
03-11-2012, 12:26 PM
NUMBER PLATE RECOGNITION
[attachment=38044]
Need of Number Plate Recognition System
This system is advanced in surveillance of cars in parking and at toll Plazas. this will automatically
generate the number of a Vehicle and that can be used on the bills or may be to monitor the usage of the
parking lot by a car. This system can be extremely useful for gathering statistics on road or at a check point
for custom checking or to recognize a stolen vehicle. This system takes a vehicle image of any size breaks
it into smaller image pieces. These pieces are then analyzed to locate the exact location of number plate in
the image. Once the area of the number plate (its x and y coordinates) is found the plate is parsed to extract
the character from it. These characters are then given to the OCR module. OCR program recognizes those
characters and converts them in text format.
Components of the system
Vehicle number plate recognition system has three main components in it.
1. Breaking the image into smaller pieces of images which are the high frequency parts of the original
image.
2. Choosing the number plate from the image pieces returned by the above module, and parsing the plate
to extract out the character part.
3. Recognizing the characters in the image pieces.
TECHNIQUE USED
Signature technique is used for the implementation of this project. Taking row wise or column wise
signature of an image gives the information about the less detail and more detail areas of the image. So it
becomes easy to find out the areas with high frequencies.
How Signature is Used
Signature technique helps in locating high frequency areas. If the image is binarised then most of the detail
is lost from the image,leaving our area of interest more prominent.
Finding Probable Number Plate In The Image
Once the image is binarised its row wise histogram (sum of white or black pixels in each row) or signature
is taken to find out which number of rows is showing ridges. These ridges are basically high frequency
areas and one of these ridges will definitely be a number plate.
Recognizing Number Plate From The Candidate Images
After this task, the x and y coordinates of all the high frequency pieces which are the candidates of
number plate are known. As we can see that on the number plate there would be 4 to seven characters. So
each character will show a ridge in the row signature of the image piece, secondly most of the information
is lost because of binarising the image so only number plate area will show maximum number of ridges.
Now if we take the row wise histogram of those binarised pieces we can see that number plate image
shows more number of ridges as compared to any other candidate image. So image with maximum number
of ridges in its row signature is chosen as the number plate.Then the same signature technique is applied to
extract the numbers from the number plate image. The difference was in the threshold value. Because here
we needed to pick each ridge in the histogram therefore the minimum value of the histogram was chosen
as the thresholding value. And the reason is that all characters might not show ridges with equal peak
(highest point in the ridge). Or a character like ‘X’ might be broken into two ridges. As it is obvious that
the center of the character X will show very small peak.
CONCLUSION
Although this system is doing the job which was its requirement, but this system can not be implemented
due to some limitations. System uses template OCR which cannot recognize the joint characters. Secondly
it recognized only those characters whose template is available to it. System performance can be
improved by using better OCRs. All those system areas where number plate recognition is implemented or
needed to be implemented require a system that recognized number plate characters form a moving
picture. Those systems take a frame form that moving picture and try to find out whether this frame
contains number plate in it or not. Where as this system takes a still image as input an tries to find out the
number plate area in it.
[attachment=38044]
Need of Number Plate Recognition System
This system is advanced in surveillance of cars in parking and at toll Plazas. this will automatically
generate the number of a Vehicle and that can be used on the bills or may be to monitor the usage of the
parking lot by a car. This system can be extremely useful for gathering statistics on road or at a check point
for custom checking or to recognize a stolen vehicle. This system takes a vehicle image of any size breaks
it into smaller image pieces. These pieces are then analyzed to locate the exact location of number plate in
the image. Once the area of the number plate (its x and y coordinates) is found the plate is parsed to extract
the character from it. These characters are then given to the OCR module. OCR program recognizes those
characters and converts them in text format.
Components of the system
Vehicle number plate recognition system has three main components in it.
1. Breaking the image into smaller pieces of images which are the high frequency parts of the original
image.
2. Choosing the number plate from the image pieces returned by the above module, and parsing the plate
to extract out the character part.
3. Recognizing the characters in the image pieces.
TECHNIQUE USED
Signature technique is used for the implementation of this project. Taking row wise or column wise
signature of an image gives the information about the less detail and more detail areas of the image. So it
becomes easy to find out the areas with high frequencies.
How Signature is Used
Signature technique helps in locating high frequency areas. If the image is binarised then most of the detail
is lost from the image,leaving our area of interest more prominent.
Finding Probable Number Plate In The Image
Once the image is binarised its row wise histogram (sum of white or black pixels in each row) or signature
is taken to find out which number of rows is showing ridges. These ridges are basically high frequency
areas and one of these ridges will definitely be a number plate.
Recognizing Number Plate From The Candidate Images
After this task, the x and y coordinates of all the high frequency pieces which are the candidates of
number plate are known. As we can see that on the number plate there would be 4 to seven characters. So
each character will show a ridge in the row signature of the image piece, secondly most of the information
is lost because of binarising the image so only number plate area will show maximum number of ridges.
Now if we take the row wise histogram of those binarised pieces we can see that number plate image
shows more number of ridges as compared to any other candidate image. So image with maximum number
of ridges in its row signature is chosen as the number plate.Then the same signature technique is applied to
extract the numbers from the number plate image. The difference was in the threshold value. Because here
we needed to pick each ridge in the histogram therefore the minimum value of the histogram was chosen
as the thresholding value. And the reason is that all characters might not show ridges with equal peak
(highest point in the ridge). Or a character like ‘X’ might be broken into two ridges. As it is obvious that
the center of the character X will show very small peak.
CONCLUSION
Although this system is doing the job which was its requirement, but this system can not be implemented
due to some limitations. System uses template OCR which cannot recognize the joint characters. Secondly
it recognized only those characters whose template is available to it. System performance can be
improved by using better OCRs. All those system areas where number plate recognition is implemented or
needed to be implemented require a system that recognized number plate characters form a moving
picture. Those systems take a frame form that moving picture and try to find out whether this frame
contains number plate in it or not. Where as this system takes a still image as input an tries to find out the
number plate area in it.