Connected component analysis integrated edge based technique for automatic vehicular license plate recognition framework. Issue 7 (6th May 2020)

• Record Type: Journal Article
• Authors: Arafat, Md Yeasir; Khairuddin, Anis Salwa Mohd; Paramesran, Raveendran
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 7(2020)
• Page Start: 712
• Page End: 723
• Publication Date: 2020-05-06
• Subjects: traffic engineering computing -- character recognition -- image segmentation -- image resolution -- feature extraction -- image recognition -- edge detection; connected component analysis integrated edge based technique -- automatic vehicular license plate recognition framework -- automatic vehicle license plate recognition -- vehicle license number -- image data -- human intervention -- effective AVLPR framework -- shaped license plates -- proper visual perception -- computational processing -- pre‐processing technique -- grey‐scaling conversion -- vertical edge densities -- horizontal edge densities -- sized license plates -- candidate region -- vertical energy mapping features -- horizontal energy mapping features -- lower resolution images -- character segmentation rate -- 400 vehicle images -- identifying recognising -- segmenting recognising -- plate number; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0006
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.