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Recognizing a license plate clearly while seeing a surveillance camerasnapshot is often important in cases where the troublemaker vehicle(s) have tobe identified. In many real world situations, these images are blurred due tofast motion of the vehicle and cannot be recognized by the human eye. For thiskind of blurring, the kernel involved can be said to be a linear uniformconvolution described by its angle and length. We propose a new de-blurringtechnique in this paper to parametrically estimate the kernel as accurately aspossible with emphasis on the length estimation process. We use a techniquewhich employs Hough transform in estimating the kernel angle. To accuratelyestimate the kernel length, a novel approach using the cepstral transform isintroduced. We compare the de-blurred results obtained using our scheme withthose of other recently introduced blind de-blurring techniques. Thecomparisons corroborate that our scheme can remove a large blur from the imagecaptured by the camera to recover vital semantic information about the licenseplate.

A New De-blurring Technique for License Plate Images with Robust Length Estimation