Dsp Numberplate Rec

7/29/2019 Dsp Numberplate Rec

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VEHICLE NUMBERPLATE RECOGNITION SYSTEM


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Information and constraints

Character recognition using moments.

Character recognition using OCR.

Signature.

Mostly implemented using hardware.


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Technique used - Signature

Take binary image.

Sum of white or black pixelsin each row and column.

Finding peaks and valleys inrow histogram or columnhistogram.

Ridge in the Row

signature

Row Histogram - Signature

Column Histogram

Signature


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Brief overview of the system

Takes image of the car and searches for the number plate in theimage.

Once the probable number plate area is located it is given to

OCR. If OCR doesnt recognize the characters from the image number

plate area is searched again from the image.

If characters are recognized then number plate search isterminated.


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Limitations of the system

Noise free image with uniform illumination.

Numbers displayed in one line on the number plate.


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Basic components of the system

Image division into small images (finding probable numberplate area in the image).

Recognizing number plate area.

Parsing number plate to extract characters. Apply OCR to the parsed characters.


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Part I

Finding probableNumber Plate images


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Finding probable number plate image

Using signature technique to break the vehicle image intosmaller image pieces.

One of these image pieces will be number plate.

Breaking image into pieces was the main issue.


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Steps towardsrefinement

Thresh holding using average of minimum and maximum value ofthe signature.


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Steps towardsrefinement Cont.

Thresh holding using average of non zero minimum and secondhighest peak of the signature.


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Thresh holding using average of minimum and minimum peak of thesignature.


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Row wise signature of binarised image


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Row wise signature of inverted binarised image


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Car Images

Original Image

Binarised image

Inverted binarised image


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Thresh holding using average of minimum and median of thesignature.


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Extracting Number plate from the image

One piece of image that will be tested for number plate


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Part II

Recognition of theNumber Plate


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Recognizing plate and parsing it

Looking for number plate in the broken pieces of vehicle image. Apply peak to valley to the candidate image pieces to further break

the image piece into possible character. Image piece with maximum peaks in candidate character is selected

as the number plate.

Column signature of the numberplate image

Column signature of the anotherimage piece


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Recognizing plate and parsing it Cont.

Column signature of the another

image piece


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Recognizing plate and parsing it Cont.

Column signature with maximum number of ridges. Percentage width of the ridge should be 15% of the whole number

plate image. Taking minimum value of the column histogram as thresh hold value.


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Parsing plate

Images of all characters


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Optical Character Recognizer -OCR


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Recognition of Characters.

Method of recognition of characters froman image containing these characters is

based on object recognition techniquesused in Digital Image Processing.

Two commonly used techniques for objectrecognition areTemplate Matching using Correlation.

Distance Measurement.


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Object Recognition Techniques.

Distance Measurement is based onrepresentation technique which usesmoments of an object.

Moments represent such measurements ofan object which can represent featuresassociated with that object, such as center ofgravity, Eccentricity.

Distance Measurement has some drawbackssuch as Extensive computations reduce efficiency of

execution of algorithm.

Difficult to implement.


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Template Matching

Another technique which was used in thisproject for character recognition is templatematching using correlation.

The technique is based on performingcorrelation between segmented image fromwhich a character is required to berecognized and character template image

which is used for recognition. This technique is efficient as compared to

distance measurement.

Only problem is associated with templateimage i.e. proper acquisition of template imageis required.


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Correlation

Modified form of convolution.

f(x,y) function represents gray scale value ata specific element (x,y) in an image.

f(x,y) represents an image from which acharacter is required to be recognized.

g(x,y) represents image of a charactertemplate.

h(x,y) represents result image aftercorrelation.


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Correlation

Correlation in spatial domain can berepresented as:

h(x,y) = f(x,y) * g*(x,y)

Correlation in frequency domain can berepresented as:

h(x,y) = f 1{f {f(x,y)} .* {g*(x,y)}}

In MATLAB correlation in frequency domain canbe easily represented as:

h=real(ifft2(fft2(f,70,324).*fft2(rot90(g,2),70,324)));


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Result of Correlation

As it is basic form of convolution, itsresult is an image which represents

convolutions of two matrices. The sizeof result matrix will be increased frominput image matrices.

Due to which we have to apply some

thresh holding on resultant image.Normally value of thresh hold is littleless than maximum value of resultant

image.


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Detection of existence of atemplate image A single pixels presence provides

information about exact match of atemplate image with input segmentedimage.

An image after thresh holding providesinformation about the presence of apixel in correlation result image.


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Modules implemented in

Matlab. Three modules were defined in MATLAB forcharacter recognition.

ocr_alpha (p1, p2, p3, p4);

ocr_numeric(p1,p2,p3,p4); P1 represents segmented image which is

required to be recognized.

P2,p3,p4 represent template images which will

be used for recognition Template(numplateimage, charimage);

Numplateimage represents the segmented image.

Charimage represents template image which is

currently used for recognition.


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Template module returns no of whitepixels in resultant correlated image.

Template function is called byocr_numeric() and ocr_alpha() functions.

Ocr_numeric() and ocr_alpha() functions

are called by user and parameters tothese functions are passed by user

Modules implemented in

Matlab.


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Thank you

Dsp Numberplate Rec

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