Number plate recognition for use in
different countries using an improved
segmentation
NCETACS 2011
ANPR – Automatic Number Plate Recognition
Transborder Traffic
Control Authorities
Car Log in
Parking areas
Road Security
(Check on notorious
Drivers in black list)
Primarily developed to cater to the needs of the law enforcement agencies.
An important figure worth mentioning in this regard is that Britain itself has 10,502 ANPR
and most of their locations are kept secret. Thames Valley police, which has released details
of spending but not locations, has put nearly £2m into 47 fixed cameras, 31 in road vehicles,
11 portable kits and one in a helicopter.
Data courtesy Guardian.co.uk
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Software part
Input image
Pre-processor
Segmentation
Unit
Recognizer
Output
Analyzer
Percentage
Accuracy
The approach do handles the entire ANPR module addressing each of the
steps but the novelty lies in the segmentation scheme adopted
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Pre-processing
Adaptive Thresholding
Image denoising
A statistical Median filter is
used to remove salt and pepper
noise from the image in gray
scale before binarizing. we
have used a 3 × 3 masking sub
window for this purpose.
Both Otsu method and Ni back’s
method were tested. Otsu method
was finally used as it is globally
adaptive which would increase
processing speed as compared to
Niback’s threshold scheme.
Without Filtration
After Filtration
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Segmentation
Clustering of white pixel zones
Component labeling of the clusters
Sorting the component clusters
MOTIVATION
Alphanumeric
characters of the
License Plate are
the ones that
have the higher
pixel count among
the pixel clusters
Directional region growing of the clusters
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Clustering of white pixel zones
Clustering
The clustering of the pixels are done on the basis of
an eight connected neighborhood of the white pixels.
Since wiener filtration was used previously so Impulse Noise was largely
eliminated, hence Algorithm works more on relevant data having less noise
Brings down
Processing time
Test Image from Jerome Coninx database
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Component labeling of the clusters
Each white cluster is labeled with a particular component tag
Component tag : Algorithm scans the entire image and
assigns a number to each cluster that it faces. The
number is initialised by 0 and incremented by one when
it jumps to the next cluster
Number of pixels in each pixel cluster is recorded against
the component tag and the position of each cluster
(corner co-ordinates) are noted
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Sorting the component clusters
Pixel count in each cluster is then
sorted in a descending manner
Number of characters (n) specified by
the Law Enforcement Agencies is
taken as the input and a buffer of
(2n-2) is set
The value of (2n-2) is determined
empirically to cope up with the over
segmented characters
Graph showing number of pixels in each cluster against the
order in which they appear after sorting
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Directional region growing of the clusters
A problem still persists that many over segmented characters that have
entered the calculation because of the buffer value (2n-2) set. Now the
need is to associate these glyphs into relevant characters
What we presently have
A sorted matrix of the pixel
clusters which has (2n-2)
number of members
A matrix containing the
positional information of the
clusters
Directional Region Growing is used based on the observation that
distance within glyphs of the same character is less than that
within glyphs of different characters
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Directional region growing of the clusters
Using the positional information (x-axis) check the dist
between the rightmost pixel of a cluster and the leftmost
pixel of the cluster next to it. If dist<dcritical
Use this pixel as seed , join the region between the two pixel
horizontally
Dilation of the joined line
Re-label the entire image using 8-connected
neighbourhood
Sort the pixel count and check the condition again
T in the upper row is
approximated by horizontal
Region growing and 7 by
Vertical region growing
The entire algorithm is repeated again using (y-axis)
distance check and comparing distances between lower
most point of upper cluster and uppermost point of
lower cluster . The process stops when minimization of
the number of characters is not further possible
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Segmentation and Normalization
Segmentation
The individual
characters are then
segmented using
bounding box
Now the glyphs do vary
Greatly in shape so ….
Segmented and normalized arranged according
to positional information
Normalization
This normalization is done
on the basis of size of the
extracted images. All of
them are scaled to [15x15]
pixels
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Recognition Module
 Automatic Neural Network (ANN) based recognition scheme
The activation function
( slope parameter in the sigmoid
function is set to 1)
Weight update function
(α is the learning term β is the momentum
Parameter E is the error term)
 It consisted of 225 input nodes
 36 output nodes (26 uppercase letters and the 10 digits)
 1 hidden layer with 300 neurons
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Database and train set
The database comprised of 150 different images of license plates used
in 58 different countries of the world.
75 images were used for training
and the rest used as test set
Entire Test Dataset Available
at www.ankushroy.webs.com
The individual pixel values were used as the input of the 15x15 binary image
of individual characters segmented
Here the module has the option of allowing the end user to select the appropriate
Images (75). Just name the countries and the network selects them from the pool
of images
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Experimentation and error analysis
The percentage accuracy here is based
on the character wise reconstruction of
the license plate after passing through
the Recognizer.
Calculated over the entire set a accuracy
of
91.59 % was reached
The skewness of the number-plate and
improper lighting condition in many cases
are the main limiting factors that affect
the recognition percentage adversely
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Prof . Anjan Kr. Rakshit, Department of Elect Engg.
Jadavpur University, Kolkata,
[1] Vehicle Registration Plates of India. Available:
http://en.wikipedia.org/wiki/Vehicle_registration_plates_of_India
[2] Ward Nicholson, “License Plate Fonts of the Western World”,
Available:http://www.leewardpro.com/articles/licplatefonts/licplate-fonts-intro.html
[3] Parking and Traffic Technologies Ltd, Smartreg ANPR,
Available:http://www.parkingandtraffic.co.uk/ANPR/smartreg-anpr
[4] J.A.G. Nijhuis, M.H ter Brugge, and K.A. Helmolt, “Car License Plate recognition with network and fuzzy
logic”, in Proc. Of IEEE International Conference on Neural Networks., volume 5, pp 2232-2236, Dec 1995
[5] Shyang-Lih Chang, Li Shein Chen, Yun-Chung Chung, and Sei-Wan Chen,
“ Automatic license plate recognition” IEEE Transaction Intelligent Transportation System, 5:42-53,2004
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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Thank You
Any Questions??
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University
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