Comparison of SVMs in Number Plate Recognition

Lihong Zheng, Xiangjian He, Tom Hintz

Research output: Book chapter/Published conference paperChapter

Abstract

High accuracy and high speed is two key issues in automatic number plate recognition (ANPR). In this paper, we construct a recognition method based on Support Vector Machines (SVMs) for ANPR. Firstly, we briefly review some knowledge of SVMs. Then the number plate recognition algorithm is proposed. The algorithm starts from a collection of samples of characters. The characters in the number plates, are divided into two kinds, namely digits and letters. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to improve recognition accuracy, two approaches of SVMs are applied and compared. Experimental results based on two algorithms of SVMs are given. From the experimental results, we can make the conclusion that 'one against one' method based on RBF kernel is better than others such as inductive learning-based or 'one against all' method for automatic number plate recognition.
Original languageEnglish
Title of host publicationProgress in Pattern Recognition
EditorsSameer Singh, Maneesha Singh
Place of PublicationLondon
PublisherSpringer
Pages152-160
Number of pages9
Edition16
ISBN (Print)9781846289446
Publication statusPublished - 2007

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