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plate recognition algorithm is proposed on the basis of a novel adaptive image
segmentation technique and connected component analysis in conjunction with
a character recognition neural network. They also describe various limitations of
current license plate recognitions methods, and discuss possible extensions that
can improve recognition capabilities and make automatic license plate recogni-
tion systems applicable to an even broader range of conditions.
To the best of our knowledge, compared with existing automatic license plate
recognition methods, HelpMe further analysis the license plate from ALPR and
correct the suspicious license plates to right ones, which makes ALPR more
reliable. Moreover, HelpMe implemented on in-memory database has favorable
scalability and eciency, especially the real-time performance.
6 Conclusions and Future Work
In this paper, a heuristic license plate correction method, named HelpMe, is
proposed for license plate correction. It can effectively correct the license plates
which are wrongly recognized by ALPR. Algorithms proposed in HelpMe are
achieved under HANA platform in order to get real-time response. Real-world
data experiments indicate that our approach greatly improves the accuracy of
ALPR, and can be employed to achieve real-time ITS applications.
In the future, we plan to enhance the robustness and applicability of HelpMe.
For instance, applying more environment variable factors in HelpMe including
but not limited to weather, lightness, average speed of vehicle, etc. And larger
real-world data sets will be applied to impove our method. Moreover, real-world
applications in ITS will further employ HelpMe to raise the accuracy and real-
time performance of ALPR.
Acknowledgement. This paper is partly supported by the National Science
Foundation of China under Grant No.91318301.
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