Abstract:
Vehicle license plate recognition is a computer vision method that aims to
automatically recognize the vehicle’s identification number from the vehicle image.
Therefore, it is an important component for automating many control and surveillance
systems, such as: road traffic monitoring, private and public entrances, highway
electronic toll collection, red light violation enforcement, and theft control. Although,
considerable researches have been carried out for vehicle license plate detection and
recognition in different countries, however, very few previous studies have been done
for the Sudanese license plate detection and recognition. Furthermore, the vehicles in
Sudan are currently being identified by traffic policemen manually. Thus, this thesis
presents a novel system approach for Sudanese vehicle license plate recognition,
which aims to improve the efficiency and the accuracy of the license plate detection
and recognition process. A simple method is proposed for detecting and extracting the
license plate, which is based on identifying the plate region by analyzing Sudanese
plate features. The plate’s skew angle is computed to guarantee the license plate
characters were accurately segmented. The character segmentation process goes
through combined techniques to improve license plate image contrast, in addition to
take advantage of the prior knowledge of Sudanese license plate. The recognition is
performed through two different recognizers, English character recognizer (Template
Matching method) and Indian digit recognizer (a novel heuristic rules based on salient
features). In order to analyze the performance and efficiency of the proposed approach
a dataset for Sudanese vehicles has been created. This dataset contains 375 vehicle
images. Using this new dataset, a number of experiments have been carried out.
Experimental results have shown that the proposed approach is efficient with accuracy
rates of 98.6% for license plate detection, 99.5 % for character segmentation, and
98.4% for recognition.
