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VEHICLE LICENSE NUMBER PLATE
RECOGNITION SYSTEM using Neuro-Fuzzy Logic
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LPR SYSTEM INVOLVES
Image capture.
Image processing
Character recognition
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FLOW DIAGRAM
InputImage
ImageBinarization
Connectedarea Labeling
Connectedarea extraction
Characterrecognition
Output
Blob Analysis
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IMAGE BINARIZATION
Blob analysis needs to analyze image in only twocolors, for finding connected areas.
Actual Image Image after Binarization
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BINARY IMAGE IN MATRIX FORM
1 1 1 0 0 0 0 0
1 1 1 0 1 1 0 0
1 1 1 0 1 1 0 0
1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0
1 1 1 0 0 0 1 0
1 1 1 0 0 1 1 0
1 1 1 0 0 0 0 0
Connectedarea
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CONNECTED AREA LABELING
1 1 1 0 0 0 0 0
1 1 1 0 2 2 0 0
1 1 1 0 2 2 0 0
1 1 1 0 0 0 3 0 1 1 1 0 0 0 3 0
1 1 1 0 0 0 3 0
1 1 1 0 0 3 3 0
1 1 1 0 0 0 0 0
Connectedarea Label
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BLOB ANALYSIS
Calculate size of each connected component.
Discard connected Area having size less than 20
pixels or greater than 100 pixels.
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CHARACTER RECOGNITION USING NEURAL NETWORK
Convert each character into character matrix
0 0 0 0 0 1 1 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
0 0 1 0 0 0 0 0 0 1 0 0
0 1 1 1 1 1 1 1 1 1 1 0 character matrix for ‘A’
0 1 0 0 0 0 0 0 0 0 1 0
0 1 0 0 0 0 0 0 0 0 1 0
0 1 0 0 0 0 0 0 0 0 1 0
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CHARACTER RECOGNITION USING NEURAL NETWORK
mat[0][1]
mat[0][2]
mat[0][3]
mat[8][7]
mat[8][8]
0
1
1
0
1
0.1247
0.2367
0.5087
0.3431
0.6491
Hidden node
Hidden node value =∑ input
node * associated weight +hidden node weight.
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CHARACTER RECOGNITION USING NEURAL NETWORK
Hidden nodes
H1
H2
H3
H4
H5
weight
weight
weight
weight
weight
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CHARACTER RECOGNITION USING NEURAL NETWORK
Calculation of output node
H1
H2
H3
H4
H5
Output node value= ∑ (output of
hidden node *associated weight )
+ output weight.
0.7650
0.5784
0.8970
0.3682
0.4563
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CHARACTER RECOGNITION USING NEURAL NETWORK
Output nodes
1
0
1
1
0
Output = ‘ W ’
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CHARACTER RECOGNITION USING FUZZY LOGIC.
Similarly recognize all characters.
For A = 0 0 0 0 1
For B = 0 0 0 1 0
For C = 0 0 0 1 1
If (output1=0 && output2=0 && output3=0 &&output4=1 && output5=0)
than character is “ B”