In the past two decades, intelligent transportation systems have been developed to improve public transportation safety and mobility by integrating multiple advanced technologies.

Automatic identification of vehicles has become more and more important in many applications; for example, parking fees, toll payments, traffic surveillance, ticket issuing, access control, and so on.

A license plate is a unique feature by which to identify each individual vehicle.

Automatic recognition of vehicle license plates, as an important research area of an intelligent transportation system, has already been widely studied for some decades.

License plates may have different formats for different countries; however, the basic techniques to recognize them are the same, i.e., license plate detection, segmentation, and character recognition.

In this paper, we aim to address the problem of Chinese car license plate recognition in traffic videos for civil use.

The Chinese car license plate consists of seven segments, where the left most segment is a Chinese character with 31 possible values indicating the region to which the car belongs.

The remaining six segments of the license plate are either numbers or alphabet letters with a total of 34 possible values; the Chinese license plate excludes letters ?O? and ?L? because they look like numbers 0 and 1.

There exist many research articles regarding car license plate recognition. The first crucial step is to detect the license plate.

The localization accuracy can greatly affect the recognition rate. Due to the presence of dense edge sets, edge-based methods are the most popular ways to localize the license plates.

Texture or the combinations of colors are also considered as key features for license plate detection.

Gendy et al. and Yanamura et al. have applied Hough transforms to detect the frames containing borders of license plates.

A principal visual word is used to automatically locate license plates. As alternative ways, morphological methods were proposed to segment license plates from original images. Kim et al. and Zhang et al. employed the genetic algorithm and AdaBoost learning algorithm, respectively, to recognize license plates. Neural network-based approaches are also frequently used.