Thesis Open Access

ETHIOPIAN CAR LICENSE PLATE RECOGNITION USING DEEP LEARNING

ERDEY SYOUM


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  <identifier identifierType="DOI">10.20372/nadre/4493</identifier>
  <creators>
    <creator>
      <creatorName>ERDEY SYOUM</creatorName>
      <affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ETHIOPIAN CAR LICENSE PLATE RECOGNITION USING DEEP LEARNING</title>
  </titles>
  <publisher>National Academic Digital Repository of Ethiopia</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Automatic Vehicle Identification Convolutional Neural Network Deep Learning Object Detection Optical Character Recognition</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Supervisor">
      <contributorName>Sreenivasa Rao.vuda (PhD)</contributorName>
      <affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-01-16</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/4493</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre/4492</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aastu</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/nadre</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The focus of this research is to develop a system that assist humans in reading car&lt;br&gt;
license plate. Such a study is important as the number of traffic on roads becomes&lt;br&gt;
increasing constantly, the manual process in car license plate recognition becomes a&lt;br&gt;
serious problem for traffic management system which not only detect and track a&lt;br&gt;
vehicle but also identify it. Initially a dataset that contains 930 car images was prepared&lt;br&gt;
for model comparison purpose. Two object detection algorithms (Faster R-CNN and&lt;br&gt;
SSD) were trained and tested on the same dataset using the same model to select the&lt;br&gt;
best candidate. The metrics for the comparison were accuracy, average prediction time,&lt;br&gt;
and total training time taken. It was found that Faster R-CNN gives high accuracy, short&lt;br&gt;
average prediction time, and short total training time. After that additional car and&lt;br&gt;
cropped license plate images were added to the prepared dataset and based on this, two&lt;br&gt;
object detection networks were trained using Faster R-CNN one for plate detection and&lt;br&gt;
another for character recognition on the detected plate. The proposed approach has been&lt;br&gt;
tested on test set and later collected images of national license plate of Ethiopia. Both&lt;br&gt;
the trained models were achieved a high accuracy which is 99 and 98.89 mAP over 0.5&lt;br&gt;
IoU for plate detection and character recognition respectively and takes on average 12s&lt;br&gt;
to complete the recognition of a license plate. The study could be further investigated&lt;br&gt;
on other countries.&lt;/p&gt;</description>
  </descriptions>
</resource>
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