access icon openaccess Symmetry features for license plate classification

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.

Inspec keywords: image recognition; optical character recognition; edge detection; gradient methods; image classification; support vector machines; feature extraction; image segmentation; text analysis; image colour analysis; video signal processing

Other keywords: symmetry features; feature matrix; global symmetry; local candidate stroke pixels; input license image; cursive text; printed texts; high recognition rate; GVF opposite direction; license plate classification; stroke width distance; license plate images; stroke pixel direction; multitype images; local symmetry; cursive texts; global candidate stroke pixels; common stroke pixels; statistical features

Subjects: Computer vision and image processing techniques; Knowledge engineering techniques; Optical, image and video signal processing; Image recognition

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