Transcript An efficient method of license plate location and

An efficient method of license plate location
Pattern Recognition Letters 26 (2005) 2431-2438
Journal of Electronic Imaging 11(4), 507-516 (October 2002)
Presented by - Waseem Khatri
Objective :
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To efficiently locate a license plate in an
image
Motivation:
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License plate recognition can be an essential
tool for
Road traffic monitoring
Automatic payments of tolls on highways &
bridges
Parking lot access control
Ticketing speeding vehicles
Algorithm
Image
Enhancement
Vertical Edge
Extraction
Noise Removal
Plate
Location
License plate
Extraction from
Original image
Edge Information
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Plate area contains rich edge information
Background areas around the plate mainly include
horizontal edges
Background areas have long curves and random noises
If only the vertical edges are extracted from the car
image and most of the background is removed, the plate
area can be isolated
Image Enhancement
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The input image is converted to a gray scale image
of size 384 X 288
Gradients in the image due to improper lighting conditions
Few vertical edges in the plate area
Enhancement is necessary
Calculate the luminance and variance of each pixel
Bilinear Interpolation
Enhancement Coefficient
8 X 8 Blocks
Edge Extraction and Noise Removal
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Vertical edge extraction using Sobel Operator
  1 0 1
 2 0 2
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 1 0 1 
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Thresholding
Background curve and noise removal is done using the
Concerned Neighborhood Pixel (CNP) Algorithm
CNP checks all pixels around the concerned pixel and decides
if it’s a randam noise pixel or a genuine edge pixel
License plate search
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A window of size (H X W) is passed through the CNP output
image
Total number of edge points in the window are counted
Candidates are selected if they are above a certain threshold
Maximum value among the candidates is considered as a final
result
The co-ordinates are noted and the plate is extracted from the
orignal image
Results:
Results:
System Application
Image
Enhancement
BLI
Output
System
Bayesian
Vertical Edge
Extraction
Classifier
Fisher
Noise Removal
CNP
Character Extraction
(Segmentation)
Hotelling
Transform
Neural
Nets
Blob
Coloring
Plate
Location
Affine
Transformation
Conclusion
Advantages
 Higher recognition rate compared to other methods like Line sensitive
filters (Luis et al., 1999), Row-wise & Column-wise DFT’s (Parisi et al.,
1998), Edge image improvement method (Ming et al., 1996)
Drawbacks
 Slower than other four methods
 Calculated values of luminance and variance using Bilinear
Interpolation are not actual values
 Image size is fixed 288 X 384
 Window size of the license plate search is fixed
 Bilinear Interpolation is the most computationally intensive procedure
in the given algorithm