License plate recognition based on pulse coupled neural networks and template matching

License plate recognition (LPR) is an important part of the vehicle detection system, which plays a significant role in traffic management and has a variety of applications. This paper presents a license plate recognition method based on pulse coupled neural network (PCNN) and template matching. One PCNN is implemented to segment the gray image containing the License plate, and another PCNN is applied to extract the characters from the located plate. The method has some advantages when the image is polluted or photographed without enough illumination. After the plate characters are extracted, they are compared with the pre-defined template using template matching. Based on the similarity between the extracted characters and template characters, the license is recognized. Simulations have been presented for illustrative purposes.