Character classification for license plate recognition system based on image processing using MATLAB
A License Plate Recognition System (LPRS) is one of the most important systems used for monitoring and controlling transportation and traffic in many countries. The LPRS is used for many purposes such as toll collection, traffic monitoring and control, smart parking, speed limiting Because of...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/66733/1/FSKTM%202016%2030%20IR.pdf |
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| Summary: | A License Plate Recognition System (LPRS) is one of the most important systems used for
monitoring and controlling transportation and traffic in many countries. The LPRS is used
for many purposes such as toll collection, traffic monitoring and control, smart parking,
speed limiting Because of its importance, LPRS should be continually studied and
improved by doing a lot of studies to solve each problem that can reduce the performance
of LPRS. One of the problems is the noise in the captured images, caused by rain and haze,
which lead the system to incorrectly recognize the characters. To address this problem,
multiple filters to reduce the noise inside the images, especially the noises which is caused
by haze and rain, have been investigated. Studies attempt to enhance the effectiveness of
the system that is used to detect and recognize car plate characters and numbers and to find the accurate algorithm most suitable for specific countries, depending on the country’s
standard car plate specifications. Because of our study done in Malaysia, and for
Malaysian car plates we should know more about Malaysian car plate design. Malaysia has
specific car plates designed with black background and white font at a fixed size. Several
applications have been developed in Malaysia to identify these plates and recognize the
characters and numbers. This research is mainly focused on comparing two such
applications. Each application uses a different algorithm and each algorithm will be tested
with the same proposed filters and dataset, which is taken under bad weather and
illumination conditions to test each algorithm’s performance in the most challenging cases. |
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