Automatic Fetching of Vehicle details using ANPR Camera

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Automatic Fetching of Vehicle details using ANPR Camera
Ankit Kumar, Adrita Roy, Koushik Pal
Department of Electronics and Communication Engineering, Guru Nanak Institute of Technology, Sodepur, West
Bengal
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Abstract – The number cars worldwide are set to double
by 2040. With the rise in number of car owners each year,
the traffic control and identification of vehicle owner is
getting tedious. On occasions where there are multiple
people breaking the traffic rules like speeding or not
having a license, the traffic personal will have a hard time
catching all of them. Automatic Number Plate Recognition
(ANPR) system, a camera-based number recognition
system which reads the number plate of multiple vehicles
at a time, comes handy in such situations. With the help of
ANPR cameras and a database with the stored
information about the vehicle owner will automatically
filter such individuals out. Technologies such as text to
speech will give impromptu notice to the traffic personal
about the car breaking the traffic rule. Doing so will
reduce the margin of error as well as give the person in
charge the Real-Time Traffic Information and enough
time to react and catch the culprit.
Key Words: ANPR System, Number recognition
system, Database, Text to speech, Real-Time Traffic
Information
1. INTRODUCTION
ANPR system is an image-processing innovation which is
used to recognises vehicles by their license plates. This
Recognition System also takes out the abnormal state
information from the digital image captured. The useless
homogeny includes the dimension and the outline of the
License Plate.
The ANPR system consists of following steps: -
i. Vehicle image capture.
ii. Pre-processing.
iii. Number plate extraction.
iv. Character segmentation.
v. Character recognition.
The initial step of ANPR system is location of the vehicle
and capturing the image of vehicle, the second step is the
localization of Number Plate and then the extraction of
vehicle Number Plate is done. The final step uses image
segmentation strategy. Segmentation is done for
individual character recognition. This sums up the
purpose of the ANPR camera in this system. Next, the
number is searched through the database available at
the traffic control room. This database includes all the
information regarding the owner. This process is
followed for multiple vehicles in the traffic at a given
time simultaneously. Finally, the vehicles disobeying the
traffics rules are marked and the numbers are sent to the
official on-duty to check. This part is done by the use to
text to speech converters used in the system.
1.1 Methodology
● The process of ANPR starts with identifying a
registration plate of the vehicle.
● It involves the algorithms used which are able to
identify the rectangular area of the registration
plate from an original picture.
● This is achieved through video cameras
capturing images that are analyzed using Optical
Character Recognition (OCR), which scans each
group of pixels within the images and estimates
whether or not it could be a letter and replaces
the pixels with the ASCII* code for the letter. (*)
● ANPR cameras need to be of a special type and
set up within certain designated parameters.
● The identification and recognition process takes
place in four phases mainly.
(1) Preprocessing of Image
(2)Localizing Registration Plate
(3) Segmentation of Characters
(4) Recognition of Actual number plate.
● The implementation is started by capturing the
number plate of the vehicle.
● When the number plate is of sufficient size for
the OCR software the frame is scanned and the
registration number is converted to ASCII code
and held in a list.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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● This continues for a series of images according
to the speed and position of the vehicle ensuring
that the optimum view of the license plate is
achieved.
● The contract extension and median filtering
techniques enhance the gray level of
registration plate image.
● Next is the character segmentation part which
further segments the character individually
from the extracted number plate.
● For easy comparison of the input character with
the character in the database the result is
normalized into the character set as the size of
the images in the database.
● Finally, it's time to apply Optical Character
Recognition.
● The optical character recognition is a
recognition method in which the input is an
image and the output is a string of character.
● Template matching is one of the approaches of
OCR.
● OCR automatically identifies and recognizes the
characters without any indirect input.
● The characters on the number plate have
uniform fonts then the OCR for number plate
recognition is less complex as compared to
other methods.
● The edge detection and gray scale filter is
applied initially as a preprocessing for selected
images to isolate the number plate region which
is a smaller part from the extracted image.
1.2 Proposed Model
● In this project, we propose an automatic and
mechanized license and number plate
recognition system which can extract the
license plate number of the vehicles passing
through a given location using image processing
algorithms.
● Using special cameras, the system takes
pictures from each passing vehicle and forwards
the image to the computer for being processed
by the ANPR software.
● Plate recognition software uses different
algorithms such as localization, orientation,
normalization, segmentation and finally
optical character recognition (OCR).
● The resulting data is applied to compare with
the records on a database.
● Experimental results reveal that the
presented system successfully detects and
recognizes the vehicle number plate on real
images.
● This system can also be used for security and
traffic control.
● This system can also be used to identify
stolen vehicles on roads.
● The images taken by these cameras are
subsequently processed in a computer.
● All vehicle traffic information is stored in the
system database for a long time.
● Thus, detailed traffic information can be
retrieved from different parking gates at
different times.
2. Equipment and system used
Multi-lane vehicle number plate recognition system can
be divided into a camera unit and enclosure unit. The
camera unit is composed of a housing, a camera, a lens,
an IR LED controller, and an IR LED board. Theenclosure
unit includes an enclosure, a controller and an SMPS The
camera is a device for acquiring images, and the IR LED
controller and the IR LED board are devices for lighting
at night or in the rain.
Camera specifications are as follows:. The number of
pixels of the image to be photographed is proportional to
the square of the number of lanes. A camera of 2.8
megapixels was used for two-lane ANPR, and in order to
obtain the same level of image quality, a camera and lens
were used for three lanes and 11.2 megapixels for four
lanes.

Fig - 01
2.1 Flow Chart and Steps
ANPR Recognization
Fig-02
Fig - 03
3. CONCLUSIONS
ANPR can provide various benefits like traffic safety
enforcement, security- in case of suspicious activity by
vehicle and immediate information availability. It can be
further extended as multilingual ANPR to identify the
language of characters automatically based on the
training data. For low resolution images some
improvement algorithms like super resolution should be
focused. Most of the ANPR focus on processing one
vehicle number plate but in real-time there can be
multiple vehicle number plates being processed.
REFERENCES
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DOI:10.359qWSA40/ijitee.H9257.0610821

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BIOGRAPHIES
Adrita Roy
Student
(Currently studying
B.Tech in
Electronics and
Communication
Engineering, GNIT,
Kolkata, India, Have
interests in different
programming
languages, playing
musical
instruments, art)
Ankit Kumar
Student
(Currently studying
B.Tech in
Electronics and
Communication
Engineering, GNIT,
Kolkata, India, Have
interests in different
programming
languages, playing
instruments and
playing cricket. )
Koushik Pal
Assistant professor,
department of ECE,
GNIT, Kolkata
(Area of research-
signal processing,
biomedical image
processing, machine
learning)

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