License Plate Recognition (LPR)
description
Transcript of License Plate Recognition (LPR)
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License Plate Recognition
(LPR)By: Mani Baghaei Fard
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Preface
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During recent years number of moving vehicles in roads and highways has been considerably increased
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Hence, Intelligent transportation systems (ITSs) have been developed as a major tool for analyzing and also handling the moving vehicles in cities and roads
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These systems attempt to facilitate the problem of identification of cars, via various techniques which mainly rely on automated (rather than manual) algorithms.
Image processing is one of these techniques
Unique property for identifying all vehicles is their license plate number.
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Security control of restricted areas
parking management systems
traffic law enforcements
surveillance systems
Electronic toll collection
Some applications:
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Difficulties: Poor image resolution usually because the plate is too far away but sometimes
resulting from the use of a low-quality camera.
Weather condition
complex background
plate deficiencies (damaged or dirty)
Supporting specific range of distances
Viewpoints
Blurry images, particularly motion blur
Poor lighting and low contrast due to overexposure, reflection or shadows
response time is another restriction in real time applications such as license plate tracking
Lack of coordination between countries or states which results in different design of the plates.
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A license plate recognition (LPR) system mainly consists of three major parts
license plate detection (LPD) character segmentation and Optical Character Recognition(OCR)
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The task of recognizing specific object (i.e. Car license plate here) in an image is one of the most difficult topics in the field of computer vision
There are many methods
license plate detection (LPD)
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Edge-based techniques:
methods based on edge analysis combined with morphology operations achieved promising results . Presence of dark characters on the light background at license plate provides strong edges which can be used as a cue to detect the license plate.
Unfortunately, solely using edge information, fails the algorithm in complex scenes. Hence, combining edge information with other cues improves the detection rate.
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Hough transform: attempts to find the rectangular shapes.
Advantages: useful in finding the boundary box of a license plate regardless of characters.
Disadvantages:
Not suitable for distorted or dirty plate
Computational complexity
is only suited for closed shut
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Texture analysis: This approach takes the advantage of existing homogenous and frequent texture-like edges in
the plate region. Gabor filters have been one of the major tools for texture
analysis Using these filters, the process is independent of rotation
and scaling. It has the ability of studying images in an unlimited number of directions. But it is a time consuming
and complex method specially when applied to large images.
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Fuzzy Logic-based Texture – based Neural networks Train and test techniques such as Adaboost And many others….
Definitely ,I am not going to details about all of them …!!!
Other methods:
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An optimized Edge-based method
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By observing license plates in images, two main features are noticed:
1) horizontal edges around a car plate are relatively strong and dominant.
2) density of vertical edges across a car plate are significant.
Methodology
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These two important features and also low complexity for edge-based analysis motivate us to use edge information for the car plate detection.
So let`s do it step by step!
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Step 1: RGB2GRAY
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RGB
Gray
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Step 2: Edging
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Edging wrt to what axes is more efficient?
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Horizontal Edges
Vertical Edges
Vertical & Horizontal Edges
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Wise Idea
Estimating the location of plate with significant density of vertical edges
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Roberts
Log
Zero cross
Canny
Prewitt
Sobel
Recall: Edging Methods
Can find Vertical and Horizontal edges seprately
By experience Sobel Preferred Cause of better response
Sobel Operator is a [1 2 1] filter
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Result of Step 2
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Step 3 :Enhancing Plate-like regions in order to have better
response in these areas
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a major cause of failure for a plate detectionsystem is low quality of car image. In order to improve the quality of plate image I used a pre-processing algorithm which increases the image contrast at locations where might be a license plate.
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variance of local intensity for constituting pixels of the license plate has a limited range and does not change dynamically. This function increases image
Zheng et al. method
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Based on some experiments the local intensity variance for a plate region can be out of considered range 0–60.
method does not work well under severe illumination change.
Drawbacks:
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He replaced the variance of image intensity with the density of vertical edges in Zheng`s method
Vahid Abolghasemi`s method
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What logic has robust response in plate like
region?
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Histogram Analyzing method
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How about this example?
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Mission failed again !!!
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1-Reading image 2-RGB2gray 3-find out vertical edges using Sobel operator 4-Dilation along X axes 5-smoothing 6-using morphological tools to extract plate 7-enhancement and plate preparation for
OCR algorithm
I tried to find out another way