30-05-2012, 03:10 PM
Number Plate Recognition for Use in Different Countries Using an Improved Segmentation
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INTRODUCTION
The automatic number plate recognition systems (ANPR)
exist for a long time, but only in the late 90s it became an
important application because of the large increase in the
number of vehicles. The information extracted from the
license plates is mainly used for traffic monitoring, access
control, parking, motorway road tolling, and border control,
making car logs for parking systems, journey time
measurement etc. by the law enforcement agencies.
The recognition problem is generally sub-divided into 5
parts: (1) image acquisition i.e. capturing the image of the
license plate (2) pre-processing the image i.e. normalization,
adjusting the brightness, skewness and contrast of the image
(3) localising the license plate (4) character segmentation i.e.
locating and identifying the individual symbol images on the
plate, (5) optical character recognition. There may be further
refinements over these (like matching the vehicle license
number with a particular database to track suspected vehicles
etc.) but the basic structure remains the same. A guiding
parameter in this regard is country-specific traffic norms and
standards.
Contribution of our work
After going through the existing literature it was seen that
the Hough transform and the projection based scheme have
been extensively used as the segmentation algorithm but there
has been some shortcomings in both of these methods. The
projection based method may be susceptible to error due to
predefined threshold as shown. Considerable computational
speed maybe compromised due to large memory requirement
of the Hough transform process. Another noteworthy point is
that a country-independent general framework was not
developed in many of those studies. Most of the work is
country specific [10, 11, 12,13] i.e. it will work for a particular
countries traffic rules but will fail in case of other countries.
RECOGNITION MODULE
A multiple layer perception (MLP) neural network was
used in the supervised learning mode. It consisted of 225 input
nodes i.e. the 225 pixel values of the training image, and the
output nodes consisted of 36 nodes i.e. the 36 classes (26
uppercase letters and the 10 digits). It was observed that
number plates use mostly uppercase letters so only uppercase
letters were considered. The neural network had only 1 hidden
layer with 300 neurons in it. A sigmoid function (explained in
the next section) was used as the activation function of the
network. This neural network was based on the general
gradient-descent algorithm.
CONCLUSION AND FUTURE RESEARCH
In this paper we have presented a new method of
segmenting the characters of the license plate based on a
majority pixel value data. We have also addressed the issue of
building the databases as per user convenience so that the user
has the option to train the neural network with the fonts those
are more relevant and mostly used in any particular
geographical location. This is totally optional i.e. The user can
change the network if they want to for better results. This
algorithm has been tested on 150 images and it is found that
the accuracy of the system is about 91.59%. The major sources
of error were the skewness of the number plate and extreme
variation in illumination conditions, which can be aptly
removed by enhancing the approach further.