Abstract:
With the exponential increase in the number of private and public transportation systems,
automatic detection and recognition of car license plates has translated into a
pivotal technology that has enabled e ective management of modern cumbersome terrestrial
tra c. The technology has several diverse applications such as tra c monitoring,
law enforcement, real time intelligence gathering, border management, intelligent transportation
systems etc. At the same time there are many typical and challenging problems
linked to it such as the variety of plate formats, varying geometry, non-uniformity in illumination,
diverse weather conditions, complexity of backgrounds etc. The research
brought out in this dissertation puts forward a novel method to counter yet another challenge
for implementation of license plate recognition for real time tra c typically in an
expressway environment, encompassing critical issues such as computation complexity
and computation time, without compromising accuracy. Focus has been given on the
enhancement of those sub-modules of License Plate Detection systems which are primary
contributors towards processing time and computational complexity. A novel idea of a
hybridized approach to counter the issues of cross-platform integration and
exibility has
been put forward, wherein the accuracy of algorithm proposed will not be dependent on
the resolution of the input image.
To further e ectively leverage the results of successful detection of the license plate, a
novel technique of character segmentation for accurate and e cient extraction of license
plate number from the detected license plate has also been proposed. The proposed character
segmentation technique is designed keeping in focus the issues related to adaptability
to variable plate backgrounds and illumination without compromising the factors of
processing time and computation complexity.
