An algorithm for accuracy enhancement of license plate recognition

This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video.

► Global edge features and local Haar-like features are combined to construct a cascaded classifier to detect license plates. ► An improved blob detection algorithm is applied to enhance the characters in license plates. ► A modified OCR is used to read the characters. ► The proposed system is robust under poor illumination conditions and efficient in real-time applications.