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Number plate recognition for use in different countries using an improved segmentation. Ankush Roy, Debarshi Patanjali Ghoshal Department of Elec. Engg . Jadavpur University. NCETACS 2011. Why ANPR ?. ANPR – A utomatic N umber P late R ecognition. Transborder Traffic - PowerPoint PPT Presentation
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Number plate recognition for use in different countries using an improved segmentation
NCETACS 2011
ANPR – Automatic Number Plate Recognition
Transborder TrafficControl AuthoritiesTransborder TrafficControl Authorities
Car Log inParking areasCar Log inParking areas
Road Security (Check on notoriousDrivers in black list)
Road Security (Check on notoriousDrivers in black list)
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 2
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
Input image Pre-processor
Analyzer
Recognizer
Segmentation
UnitOutput
PercentageAccuracy
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 3
Software part
Pre-processing
Image denoising
A statistical Median filter is used to remove salt and peppernoise from the image in grayscale before binarizing. we have used a 3 × 3 masking sub window for this purpose.
Adaptive Thresholding
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 4
Segmentation
Clustering of white pixel zones
Component labeling of the clusters
Sorting the component clusters
Directional region growing of the clusters
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 5
MOTIVATIONAlphanumeric characters of the License
Plate are the ones that have the
higher pixel count among
the pixel clusters
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 downProcessing time
Test Image from Jerome Coninx database
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 6
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 7
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
Graph showing number of pixels in each cluster against the order in which they appear after sorting
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 8
The value of (2n-2) is determined empirically to cope up with the
over segmented characters
Directional region growing of the clusters
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 9
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 haveA 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 10
Directional region growing of the clusters
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
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
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
T in the upper row isapproximated by horizontalRegion growing and 7 by Vertical region growing
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 11
Segmentation and Normalization
Segmentation
The individual characters are then segmented using
bounding box
Now the glyphs do vary Greatly in shape so ….
NormalizationThis normalization is done on the basis of size of the extracted
images. All of them are scaled to [15x15] pixels
Segmented and normalized arranged according to positional information
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 12
Recognition Module
Automatic Neural Network (ANN) based recognition scheme
It consisted of 225 input nodes
36 output nodes (26 uppercase letters and the 10 digits)
1 hidden layer with 300 neurons
The activation function
Weight update function(α is the learning term β is the momentumParameter E is the error term)
( slope parameter in the sigmoid function is set to 1)
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 13
Database and train set
The database comprised of 150 different images of license plates used in 58 different countries of the world.
Entire Test Dataset Availableat www.ankushroy.webs.com
75 images were used for training and the rest used as test set
The individual pixel values were used as the input of the 15x15 binary imageof individual characters segmented
Here the module has the option of allowing the end user to select the appropriateImages (75). Just name the countries and the network selects them from the pool of images
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 14
Experimentation and error analysis
The percentage accuracy here is based on the character wise reconstruction of the license plate after passing through the Recognizer.
The skewness of the number-plate and improper lighting condition in many cases are the main limiting factors that affect the recognition percentage adversely
Calculated over the entire set a accuracy
of 91.59 % was reached
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 15
Thank You
Any Questions??
Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University 16