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AUTOMATIC LICENSE PLATE
RECOGNITION (ALPR)
M G Niketh , 08ec33
INTRODUCTION
Reading a license plate is the first step in determining the
identities of parties involved in traffic incidents. An efficient
automatic license plate recognition process may become the core
of fully
computerized road traffic monitoring systems
electronic fee collection solutions
surveillance devices
safety supervision systems.
It is important that the recognition accuracy of such a process is
very high. Tracking and registering dangerous behavior in traffic
may be used for prosecuting offenders.
ASSUMPTIONS
Input is an image of a stationary Car.
Only the most common type of license plates (single line) will be
dealt with.
The license Plate has a White background with text written in
Black.
PROCEDURE
License-Plate Recognition System consists of following main
modules:
License plate detection,
Character segmentation and
Cleaning phase
Classification phase
LICENSE PLATE EXTRACTION
The license plate region is characterised by a row of
transitions from dark to light and vice versa (we call such a
transition an edge).
Algorithm
First Convert from RGB to grayscale
Generate edge image corresponding to grayscale image
( Sobel edge detection )
Perform row profiling to determine the Y coordinates of
license plate region .
Segment the edge image along this (y1-y2 region)
Perform column profiling to determine the X coordinates of
license plate region .
Segment the grayscale images along the X and Y set of
coordinates so obtained above.
Row profiling
Y region
Column profiling
X region
More edges here
SEGMENTATION PHASE
Column that contains part of a
character is darker than a column
that contains the background of the
license plate .
Algorithm
Perform Column profiling .
( Multiple broad peaks obtained
for different characters )
Segment the images along the
lines between the peaks , to
obtain character bitmaps .
CLEANING PHASE
Removal of all dark pixels from the character bitmap that do not belong to the characters.
Eg. Black bars above and below the characters ,dirt spots, or nuts and bolts.
Algorithm
Convert from grayscale to binary
Remove all rows and columns containing only white or only Black pixels , or say (99 % white, black pixels )
Remove all small groups of black pixels .
The cleaned character bitmaps. All dark pixels that are not part
of the character are removed
CLASSIFICATION PHASE (FEATURE DETECTION)
Determine the character type from the character
bitmap obtained based on the unique features
each character possesses .
Algorithm
Perform the row profiling to obtain ‘row peaks’
Perform the column profiling to obtain ‘column
peaks’.
Find number of junctions of the character.
Find number of end points of the character.
2 column peaks
+ 3 row peaks
The character
could be any of
{5,6,8,9,B,G}
1 End point
+ 1 junction Row(end point) is higher than
row(junction). Thus character is ‘6’
TEMPLATE MATCHING
Alternative solution to the classification phase is –
Algorithm
The character bitmap is contrast enhanced .
All the character bitmaps are normalised and fit into a
box of the size of the standard templates .
Find cross correlation (measure of similarity) between
the set of templates and the obtained character bitmap
The character is matched to the one with the highest
CF value .
TEMPLATE
DATABASE
DRAWBACKS
DRAWBACKS
The algorithm so discussed fails to properly
differentiate between (B,8) (0,D) (2,Z).
The algorithm assumes that there are not more
than 1 candidate for license plate region .
SOLUTION:
Use the Aspect ratio of the standard license plates to
obtain the right candidate among many )
Compare the edge density (number of edges per unit
area) of the various candidates .The highest one is the most
appropriate one .
CONCLUSION
What is trivial for the human eye may appear a very difficult task
for the computer, but still computer vision is very powerful tool
that provides us the capabilities to perform very useful operations
as the one we implemented in this project.
Application
•Parking lot management
•Automatic toll collection enforcement
•Traffic enforcement statistics
•Border surveillance
•Stolen vehicle search