License plate recognition using DTCNNs

Abstract

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.