Determining adaptive thresholds for image segmentation for a license plate recognition system

Siti Norul, Khairuddin Omar, Khairuddin Omar, Abbas Salimi, Maria Petrou, Marzuki Khalid
<span title="">2016</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
A vehicle license plate recognition (LPR) system is useful to many applications, such as entrance admission, security, parking control, airport and cargo, traffic and speed control. This paper describe an adaptive threshold for image segmentation applied to a system for Malaysian intelligent license plate recognition (MyiLPR). Due to the different types of license plates used, the requirements of an automatic LPR system are rather different for each country. Upon receiving the input car image,
more &raquo; ... his system (MyiLPR) detects and segments the license plate based on proposed adaptive threshold via image and blob histogram, and blob agglomeration, and finally, it extracts geometric character features and classifies them using neural network. The use of the proposed adaptive threshold increased the detection, segmentation and recognition rate to 99%, 94.98% and 90% correspondingly, from 95%, 78.27% and 71.08% obtained with the fixed threshold used in the originally proposed system.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2016.070667">doi:10.14569/ijacsa.2016.070667</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mnpe5hdsxrfvngdhq54daxzeuy">fatcat:mnpe5hdsxrfvngdhq54daxzeuy</a> </span>
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