PROPOSED METHOD

The first step after image acquisition is to convert the image into a gray-scale image. Then this gray-scale image is converted into a binary(black and white) image for further morphological operations.

We first remove all the connected components lesser than or equal to 30 so that we can remove noise from the number plate such as dust particles,dirt,etc.Next we remove all the connected components lesser than 3000 or 3500 depending on the size of the image. This step removes the digits from the number plate as shown in the figure.

Next we obtain only the digits on the number plate from the acquired image by subtracting the original image from the image obtained in the previous step. This gives the digits on the number plate as shown in the figure.

As of now we have obtained the digits on the number plate. But, we can't start character recognition on this image directly. We need to process this image further to be able to get accurate results. The next step is to remove the connected components lesser than 250(can vary) so as to remove any other kind of noise left such as bolts on the number plate or dents,etc.

We erode the image using the disk structure, so that we can separate two or more characters if they are connected to each other and also this steps helps us in removing noise in the image. We then dilate this image to restore the strength of the characters which will be helpful for template matching.

After the above steps, the image we we obtained can be used for character recognition. We can identify each character individually using the bounded box technique. We get each character separately after applying the bounded box technique. Sometimes we may not get all the characters separately using bounded box technique when the characters in the number plate are not separated in the number plate, one more round of erosion in these cases will help us to get all the characters separately.

We use template matching recognition of the separately identified characters from the previous step. We have a set 62(10 numbers and 52 alphabets) images of size '42x24' . We perform template matching on each of the characters identified from the number plate using the images present in the database. We kept to threshold of 0.35 on the co-relation value. Any character for which we get a value greater than 0.35 we select that character as the matched character.

 RESULT: