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TRANSCRIPT
License Plate Recognition System
ABSTRACT:
License plate detection is widely considered as a solved problem with
many systems already in operation. However, the existing algorithms or systems
work well only under some controlled conditions. There are still many challenges
for license plate detection in open environment, such as various observation angles,
background clutter, scale change, multi-plate, uneven illumination, and so on. In
this paper, we propose a novel scheme to automatically locate license plate by
principal visual word discovery and local feature matching. Observing that
characters in different license plates are duplicates of each other, we bring in the
idea of bag-of-words (BoW) model popularly applied in partial-duplicate image
search. Unlike the classic BoW model, for each plate character, we automatically
discover the principal visual words characterized with geometric context. Given a
new image, the license plates are extracted by matching local features with
principal visual words. Besides license plate detection, our approach can also be
extended straightforward to detection of logos and trademarks.
Image Capturing
Extraction of plate regionand
Edge detection
Image Segmentation
Character recognition of Plate
Due to the invariance virtue of SIFT feature, our method can adaptively deal with
various changes of license plate, such as rotation, scaling, illumination, etc.
Promising results of the proposed approach are demonstrated with experimental
study in license plate detection.
Block Diagram:
EXISTING SYSTEM:
License plate detection is widely considered as a solved problem
with many systems already in operation. Nevertheless, the existing algorithms or
systems work well only under some controlled conditions. For instance, some
systems require sophisticated video capture hardware possibly combined with
infrared strobe lights, or require the images taken with little distortion from view
point changes. Although many reported results are very good, with even perfect
accuracy on their test datasets, it is still a challenge task to detect license plates in
open environment.
DISADVANTAGES OF EXISTING SYSTEM:
Generally, a license plate detection system has to solve two problems:
where a license plate is located and how big it is. Usually, the candidate position of
characters in license plate is first identified, and the bounding box of the license
plate is determined later. There are many challenges in license plate detection in
open environment, such as various observation angles from cameras, background
clutter, different sizes of license plate, poor image quality from the uneven lighting
condition, and multi-plate detection.
PROPOSED SYSTEM:
In this paper, we experiment on two types of license plate. It is straightforward to
make extension to detection of other types of license plates in other regions or
countries of the world. Our main contributions include:
1) We propose an automatic method to discover the principal visual words for each
character in license plate. Each principal visual word is characterized with both
local descriptor and some other geometric clues.
2) Based on visual matching with principal visual words, we propose an efficient
scheme to accurately locate the image patch containing license plate.
ADVANTAGES OF PROPOSED SYSTEM:
In this paper, we propose an automatic approach for license plate detection in open
environment. Our approach is based on principal visual word discovering and
visual word matching. We identify the principal visual words of each character.
With the invariance merit of SIFT feature, our approach is effective in dealing with
various observation angles, scale change, and illumination variation, etc. Besides,
our approach can detect multiple plates in an image automatically. Promising
results are shown on two evaluation datasets.
MODULES: Principal Visual Word Generation
Visual Word Matching
License Plate Locating
PLATEREGION EXTRACTION:
SEGMENTATION:
MODULES DESCRIPTION:
Principal Visual Word Generation
In license plates, there are a certain number of sorted characters, each with the
same format but maybe undergoing illumination change or affine transformation.
Since SIFT feature is invariant to changes in scale and rotation, and robust to
illumination change and affine distortion [2], there exist some repeatable and
distinctive SIFT features to each character, called principal visual word (PVW).
Visual Word Matching
Given a test image, we will discover those characters with features matched to the
principal visual words. We first extract SIFT features from the test image. Then
each SIFT feature is compared with the principal visual words of each character. A
feature is considered as a candidate match if the minimum descriptor distance to a
certain principal visual word of a certain character is less than a constant threshold.
License Plate Locating
Once the character features in the test image are identified, we can make use of the
geometric context of the matched principal visual words to locate the license plate.
A bounding box will be estimated to encompass license plate by determining the
upper, lower, left and right bounding lines sequentially.
PLATEREGION EXTRACTION:
Plate region extraction is the first stage in this algorithm. Image captured from the
camera is first converted to the binary image consisting of only 1’s and 0’s (only
black and white) by thresholding the pixel values of 0 (black) for all pixels in the
input image with luminance less than threshold value and 1 (white) for all other
pixels. Captured image (original image) and binarized image are shown in Figures
Fig.1 Captured Image
Fig. 3 Locations of plate characters
Fig. 3 Number Plate Image
SEGMENTATION:
In the segmentation of plate characters, license plate is segmented into its
constituent parts obtaining the characters individually. Firstly, image is filtered for
enhancing the image and removing the noises and unwanted spots. Then dilation
operation is applied to the image for separating the characters from each other if
the characters are close to each other. After this operation, horizontal and vertical
smearing are applied for finding the character regions.
HARDWARE REQUIREMENTS
Processor : Any Processor above 500 MHz.
Ram : 128Mb.
Hard Disk : 10 GB.
Compact Disk : 650 Mb.
Input device : Standard Keyboard and Mouse.
Output device : VGA and High Resolution Monitor
SOFTWARE REQUIREMENTS
Operating System : Windows XP.
Coding Language : JAVA