23-04-2014, 01:05 PM
Automatic Number Plate Recognition
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Abstract
Automatic Number Plate Recognition (ANPR) is a mass surveillance system that
captures the image of vehicles and recognizes their license number. ANPR can be assisted in the
detection of stolen vehicles. The detection of stolen vehicles can be done in an efficient manner
by using the ANPR systems located in the highways. This paper presents a recognition method in
which the vehicle plate image is obtained by the digital cameras and the image is processed to get
the number plate information. A rear image of a vehicle is captured and processed using various
algorithms. In this context, the number plate area is localized using a novel „feature-based
number plate localization‟ method which consists of many algorithms. But our study mainly
focusing on the two fast algorithms i.e., Edge Finding Method and Window Filtering Method for
the better development of the number plate detection system
Introduction
Most of the number plate localization algorithms merge several procedures, resulting in
long computational (and accordingly considerable execution) time (this may be reduced by
applying less and simpler algorithms). The results are highly dependent on the image quality,
since the reliability of the procedures severely degrades in the case of complex, noisy pictures
that contain a lot of details. Unfortunately the various procedures barely offer remedy for this
problem, precise camera adjustment is the only solution. This means that the car must be
photographed in a way that the environment is excluded as possible and the size of the number
plate is as big as possible. Adjustment of the size is especially difficult in the case of fast cars,
since the optimum moment of exposure can hardly be guaranteed. Number Plate Localization on
the Basis of Edge Finding: The algorithms rely on the observation that number plates usually
appear as high contrast areas in the image (black-and-white or black-and-yellow). First, the
original car image in color is converted to black and white image grayscale image as shown in
figure 1.
Number Plate Localization on the Basis of Window Filtering:
The drawback of the above solution (Edge Finding Methodology) is that after the filtering
also additional areas of high intensity appear besides the number plate. If the image contains a lot
of details and edges (example: complex background) the further areas. As a result, the SFR curve
exhibits a smaller increment at the number plate and the edges in the surrounding areas may
sometimes be more dominant.
Character Segmentation
Segmentation is one of the most important processes in the automatic number plate
recognition, because all further steps rely on it. If the segmentation fails, a character can be
improperly divided into two pieces, or two characters can be improperly merged together. We
can use a horizontal projection of a number plate for the segmentation, or one of the more
sophisticated methods, such as segmentation using the neural networks. In this segmentation we
use two types of segmentation: 1. Horizontal segmentation 2. Vertical segmentation. First we
have performed vertical segmentation on the number plate then the characters are vertically
segmented. After performing vertical segmentation we have to perform horizontal segmentation
by doing this we get character from the plate.
Character Recognition
We have to recognize the characters we should perform feature extraction which is the
basic concept to recognize the character. The feature extraction is a process of transformation of
data from a bitmap representation into a form of descriptors, which are more suitable for
computers. The recognition of character should be invariant towards the user font type, or
deformations caused by a skew. In addition, all instances of the same character should have a
similar description. A description of the character is a vector of numeral values, so called
descriptors or patterns.
Conclusion
This paper presents a recognition method in which the vehicle plate image is obtained by
the digital cameras and the image is processed to get the number plate information. A rear image
of a vehicle is captured and processed using various algorithms. Further we are planning to study
about the characteristics involved with the automatic number plate system for better
performance.