License plate recognition (LPR) is the core module of an intelligent transportation system.LPR algorithms are generally composed of the following three processing steps: 1) detection of the license plate region; 2) segmentation of the plate characters; 3) recognition of each character.License plate detection is the key step of LPR

and its result directly determines the performance of the LPR system.Most of current license plate detection methods employ single features

such as the edge feature

the structure feature or the color feature

to locate the license plate

and cannot obtain satisfactory results.In order to improve the accuracy and speed of the license plate detection

and to reduce the false detection rate

we propose a license plate detection method based on multiple features. First

edge density information is used to remove most of the background area

which can greatly improve the speed of the detection process.We divide the image into small cells

compute the edge density of each cell

and remove the cells whose edge density are too large or too small.Then we use the distribution information of the license plate characters to precisely detect license plates in the remaining regions.Coupled morphological operators are used to employe the character regions and Hough transformation is used to obtain the position of the license plate.After that

we segment the license plate into characters and get the Histogram of Gradient (HOG) features of each character.The HOG features of each character are used to verify whether the character is a rightful license plate character (letter or digit).If there are more than five rightful characters in the license plate candidate

the candidate is regarded as a true plate. We establish a dataset that contains 9980 high-resolution images

and test our algorithm in three ways

that is

detect license plate by 1) context and structure information

2) structure and part information

3) context

structure and part information.The experimental results show that by employing the context information

most of the background areas can be filtered and the detection speed can be improved by the structure and part information

most of the false candidates can be removed. The detection rate of our method is 97.9%

and the average detection time is 16.3 ms. License plate detection is the fundamental step of LPR systems.Motivated by the fact that people detect objects by multiple features of the object

we propose a license plate detection method to detect license plate by combining multiple features of the license plate.The context information is used to filter most of the background areas and improve the detection speed

and the structure and part information is employed to remove most of the false candidates.The experimental results show that the proposed method can detect license plate accurately and fast under various conditions.