EDF-LPR: a new encoder–decoder framework for license plate recognition

Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license p...

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Veröffentlicht in:IET intelligent transport systems Jg. 14; H. 8; S. 959 - 969
Hauptverfasser: Gao, Fei, Cai, Yichao, Ge, Yisu, Lu, Shufang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.08.2020
Schlagworte:
accurate candidate characters
ALPR codec framework
automatic license plate recognition
candidate license plate characters
character recognition
decoding
detection rate
EDF‐LPR
edge detection
encoder–decoder framework
feature extraction
final recognition result
image recognition
image segmentation
learning (artificial intelligence)
license plate detection
object detection
object recognition
pattern clustering
recognition rate
Research Article
standard format classification
traffic engineering computing
object recognition
standard format classification
detection rate
object detection
character recognition
traffic engineering computing
decoding
EDF-LPR
automatic license plate recognition
encoder–decoder framework
pattern clustering
final recognition result
image segmentation
feature extraction
accurate candidate characters
license plate detection
edge detection
learning (artificial intelligence)
image recognition
ALPR codec framework
candidate license plate characters
recognition rate
ISSN:1751-956X, 1751-9578
Online-Zugang:Volltext
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Abstract Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license plate detection and standard format classification may fail. In this study, a new ALPR codec framework named EDF-LPR is presented. As for the encoder, at the first stage, candidate license plate characters are detected and recognised directly without considering the format of license plate, and candidate regions of characters are extracted by density-based spatial clustering of applications with noise-like algorithm; at the second stage, poor regions are processed by tilt correction and scale normalisation to obtain more accurate candidate characters. As for the decoder, a sequence learning model is trained to convert each unordered coded sequence into a sequence composed of marks that indicate a way to construct the final result string. Experiments are designed to evaluate the performance of EDF-LPR on both detection rate and recognition rate. The experimental results on public datasets show that the detection rate and recognition rate are 99.51 and 95.3%, respectively, at about 40 fps.
AbstractList Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license plate detection and standard format classification may fail. In this study, a new ALPR codec framework named EDF-LPR is presented. As for the encoder, at the first stage, candidate license plate characters are detected and recognised directly without considering the format of license plate, and candidate regions of characters are extracted by density-based spatial clustering of applications with noise-like algorithm; at the second stage, poor regions are processed by tilt correction and scale normalisation to obtain more accurate candidate characters. As for the decoder, a sequence learning model is trained to convert each unordered coded sequence into a sequence composed of marks that indicate a way to construct the final result string. Experiments are designed to evaluate the performance of EDF-LPR on both detection rate and recognition rate. The experimental results on public datasets show that the detection rate and recognition rate are 99.51 and 95.3%, respectively, at about 40 fps.
Author Cai, Yichao
Lu, Shufang
Gao, Fei
Ge, Yisu
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  surname: Gao
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  organization: Department of Computer Science, Zhejiang University of Technology, Hangzhou, People's Republic of China
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Issue 8
Keywords object recognition
standard format classification
detection rate
object detection
character recognition
traffic engineering computing
decoding
EDF-LPR
automatic license plate recognition
encoder–decoder framework
pattern clustering
final recognition result
image segmentation
feature extraction
accurate candidate characters
license plate detection
edge detection
learning (artificial intelligence)
image recognition
ALPR codec framework
candidate license plate characters
recognition rate
Language English
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Snippet Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is...
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wiley
iet
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StartPage 959
SubjectTerms accurate candidate characters
ALPR codec framework
automatic license plate recognition
candidate license plate characters
character recognition
decoding
detection rate
EDF‐LPR
edge detection
encoder–decoder framework
feature extraction
final recognition result
image recognition
image segmentation
learning (artificial intelligence)
license plate detection
object detection
object recognition
pattern clustering
recognition rate
Research Article
standard format classification
traffic engineering computing
Title EDF-LPR: a new encoder–decoder framework for license plate recognition
URI http://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0253
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