NDLI: Symmetry features for license plate classification

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Symmetry features for license plate classification

Content Provider	IET Digital Library
Author	Raghunandan, Karpuravalli Srinivas Shivakumara, Palaiahnakote Padmanabhan, Lolika Kumar, Govindaraju Hemantha Lu, Tong Pal, Umapada
Abstract	Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition (OCR) can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow (GVF) for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine (SVM) classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.
Starting Page	176
Ending Page	183
Page Count	8
Volume Number	3
e-ISSN	24682322
Issue Number	Issue 3, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/trit/3/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/trit.2018.1016
Journal	CAAI Transactions on Intelligence Technology
Publisher	The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence
Publisher Date	2018-07-23
Access Restriction	Open
Rights License	Creative Commons Attribution-No Derivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Subject Keyword	Common Stroke Pixel Computer Vision And Image Processing Technique Cursive Text Cursive Texts Edge Detection Feature Extraction Feature Matrix Global Candidate Stroke Pixel Global Symmetry Gradient Method GVF Opposite Direction High Recognition Rate Image Classification Image Colour Analysis Image Recognition Image Segmentation Input License Image Knowledge Engineering Technique License Plate Classification License Plate Image Local Candidate Stroke Pixel Local Symmetry Multitype Image Optical Character Recognition Optical, Image And Video Signal Processing Printed Texts Statistical Feature Stroke Pixel Direction Stroke Width Distance Support Vector Machine Symmetry Feature Text Analysis Video Signal Processing
Content Type	Text
Resource Type	Article

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