09-01-2016, 03:06 PM
Abstract
A License plate recognition (LPR) system can be divided into the following steps: preprocessing, plate region extraction, plate region thresholding,character segmentation, character recognition and post-processing. For step 2, a combination of color and shape information of plate is used and a satisfactory extraction result is achieved. For step 3, first channel is selected, then threshold is computed and finally the region is thresholded. For step 4, the character is segmented along vertical, horizontal direction and some tentative optimizations are applied. For step 5, minimum Euclidean distance based template matching is used. And for those confusing characters such as '8' & 'B' and '0' & 'D', a special processing is necessary. And for the final step, validity is checked by machine
and manual. The experiment performed by program based on aforementioned
algorithms indicates that our LPR system based on color image processing
is quite quick and accurate.
Introduction
The automatic identification of vehicles has been in considerable demand especially
with the sharp increase in the vehicle related crimes and traffic jams. It can also play a
crucial role in security zone access control, automatic toll road collection and intelligent
traffic management system. Since the plate can identify a car uniquely, it is of
great interest in recent decade in using computer vision technology to recognize a car
and several results have been achieved [2-14]. A typical LPR system can be divided into the following modules: preprocessing (including image enhancement and restoration), plate region extraction, plate region thresholding, character segmentation, character recognition and post-processing (validity checking). The first two modules, which only concern the shape and back/fore ground color of a plate and irrespective of character set in a plate, are the front end of the system. Module 4 and 5, on the contrary, are related to character set in a plate and regardless of the shape and back/fore ground color of a plate, so they are the back end of the system. Module 3, however, should take the shape and back/fore ground color of a plate as well as character set in a plate into consideration. Therefore, it is hard to say which end it can be categorized into.To develop an automatic recognition system of a car plate, a stable recognition of a plate region is of vital importance. Techniques such as edge extraction .