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generally similar system for character recognition.
This thesis interconnects theory and praxis, and
permits readers to experiment with described
principles in the demonstration model written in
Java
TM
. This was the second goal of this project. (for
more information, see project web site at
http://javaanpr.sf.net)
ANPR solution has been tested on static snapshots
of vehicles, which has been divided into several sets
according to difficultness. Sets of blurry and skewed
snapshots give worse recognition rates than a set of
snapshots, which has been captured clearly. The
objective of the tests was not to find a one hundred
percent recognizable set of snapshots, but to test the
invariance of the algorithms on random snapshots
systematically classified to the sets according to
their properties. The average recognition rate was
about 80%. The system achieves this recognition
rate over a set of static snapshots.
The recognition ability of this system can be
rapidly increased by processing data from video
sequences. The short video sequence contains
several frames of incoming vehicle. The similarity
between these frames makes whole video sequence
very redundant. This redundancy can be used to
increase recognition abilities. If the system has 80%
chance to successfully recognize one frame, then the
overall recognition rate can be up to 99%, if we
process several consequent frames from the video
sequence.
Since processing of video sequence is a difficult
computational operation, it is necessary to use an
appropriate hardware and software platform. The
combination of C-language and DSP processor
should be suitable.
Currently I am developing a modified version of
this algorithm for a practical security application in
Brno in cooperation with Faculty of Information
Technology.
Table 1: Recognition rates of the ANPR system (static
snapshots).
. Number of plates Weighted score
Clear plates 68 87.2
Blurred plates 52 46.87
Skewed plates 40 51.64
Average plates 177 73.02
Table 2: Algorithm complexity and system throughput on
AMD Opteron
TM
(1 GHz) processor architecture.
Snapshot resolution Average time
320x240 0.74 s
640x480 1.17 s
720x576 1.40 s
800x600 1.46 s
ACKNOWLEDGEMENTS
JavaANPR is a part of the research plan “Security-
oriented research in information technology, MSM
0021630528” at Brno University of Technology.
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