During recent years number of moving vehicles in roads and highways has been considerably increased
Hence, Intelligent transportation systems (ITSs) have been developed as a major tool for analyzing and also handling the moving vehicles in cities and roads
These systems attempt to facilitate the problem of identification of cars, via various techniques which mainly rely on automated (rather than manual) algorithms. Image processing is one of these techniques Unique property for identifying all vehicles is their license plate number.
Security control of restricted areas parking management systems traffic law enforcements surveillance systems Electronic toll collection
Difficulties: Poor image resolution usually because the plate is too far away but sometimes resulting from the use of a low-quality camera. Weather condition complex background plate deficiencies (damaged or dirty) Supporting specific range of distances Viewpoints Blurry images, particularly motion blur Poor lighting and low contrast due to overexposure, reflection or shadows response time is another restriction in real time applications such as license plate tracking Lack of coordination between countries or states which results in different design of the plates.
A license plate recognition (LPR) system mainly consists of three major parts license plate detection (LPD) character segmentation and Optical Character Recognition(OCR)
The task of recognizing specific object (i.e. Car license plate here) in an image is one of the most difficult topics in the field of computer vision There are many methods
Edge-based techniques: methods based on edge analysis combined with morphology operations achieved promising results. Presence of dark characters on the light background at license plate provides strong edges which can be used as a cue to detect the license plate. Unfortunately, solely using edge information, fails the algorithm in complex scenes. Hence, combining edge information with other cues improves the detection rate.
Hough transform: attempts to find the rectangular shapes. Advantages: useful in finding the boundary box of a license plate regardless of characters. Disadvantages: Not suitable for distorted or dirty plate Computational complexity is only suited for closed shut
Texture analysis: This approach takes the advantage of existing homogenous and frequent texture-like edges in the plate region. Gabor filters have been one of the major tools for texture analysis Using these filters, the process is independent of rotation and scaling. It has the ability of studying images in an unlimited number of directions. But it is a time consuming and complex method specially when applied to large images.
Fuzzy Logic-based Texture – based Neural networks Train and test techniques such as Adaboost And many others…. Definitely,I am not going to details about all of them …!!!
By observing license plates in images, two main features are noticed: 1) horizontal edges around a car plate are relatively strong and dominant. 1) density of vertical edges across a car plate are significant.
a major cause of failure for a plate detection system is low quality of car image. In order to improve the quality of plate image I used a pre-processing algorithm which increases the image contrast at locations where might be a license plate.
variance of local intensity for constituting pixels of the license plate has a limited range and does not change dynamically. This function increases image
Based on some experiments the local intensity variance for a plate region can be out of considered range 0–60. method does not work well under severe illumination change.
He replaced the variance of image intensity with the density of vertical edges in Zheng`s method