Project Reading 8: Automatic License Plate Recognition

Citation

Chang, Shyang-Lih, et al. "Automatic license plate recognition." Intelligent Transportation Systems, IEEE Transactions on 5.1 (2004): 42-53.

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Summary

License Plate Number Recognition is an important application of optical character recognition. It is used at automated toll booths, gate entries and many other points of authentication by car number plate. The key difference from sketch recognition techniques here is that the input is never available in the form of a sketch or stroke points. The input is always in the form of an image capture of the vehicle. So the task of number recognition is divided into two phases:

1) Locating the number plate of a vehicle in a given capture of the car.
2) Predicting the number present on the number plate.

Overall the process flowchart is as follows:

Once the input license plate image is formed, it is passed through preprocessing steps where first image is converted to a binary pixel image, i.e. only white and black colors are kept. After that character segmentation is done. After character segmentation we achieve images of individual characters which can be used to extract useful features.

Discussion

The first step in feature extraction is constructing the contour of the character. Once the contour is formed. The contour lines are then used to form a set of points equidistantly spaced on the contour lines. These points are then used to match the features against a set of templates. Kohonen SO neural models is used to form the classification step. An example of feature extraction and template matching is shown below: