Automatic vehicle license plate recognition (AVLPR) aims at extracting the region that contains the information of vehicle license number out of an image data and then identifying the characters apart from the human intervention. This study proposed an effective AVLPR framework where detection, segmentation and recognition of various shaped license plates have been focused. For both proper visual perception and computational processing, a pre‐processing technique including grey‐scaling conversion combined with close arithmetic‐based dilation has been defined. Both vertical and horizontal edge densities have been enumerated by kernel matrices which enable robustness in detecting various shaped and sized license plates. For better detection of candidate region, the vertical and horizontal energy mapping features combined with Gaussian smoothing filter have been used to enable detection of license plates from both high definition and lower resolution images under various illumination conditions and crowded background. For ensuring a better character segmentation rate which is the prerequisite for higher recognition rate, a blob assessment method has been defined integrated with connected component analysis. With 400 vehicle images having varying pixels, the proposed algorithm achieves 96.5, 95.6 and 94.4% accuracy, respectively, in identifying, segmenting and recognising the plate number.
Connected component analysis integrated edge based technique for automatic vehicular license plate recognition framework
IET Intelligent Transport Systems ; 14 , 7 ; 712-723
2020-07-01
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
horizontal energy mapping features , automatic vehicular license plate recognition framework , vertical energy mapping features , character recognition , plate number , edge detection , image resolution , vehicle license number , traffic engineering computing , horizontal edge densities , connected component analysis integrated edge based technique , character segmentation rate , effective AVLPR framework , grey‐scaling conversion , segmenting recognising , sized license plates , 400 vehicle images , image segmentation , identifying recognising , candidate region , image data , shaped license plates , computational processing , lower resolution images , feature extraction , image recognition , vertical edge densities , pre‐processing technique , human intervention , proper visual perception , automatic vehicle license plate recognition
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