A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

Anagnostopoulos, Christos; Anagnostopoulos, Iraklis; Loumos, V.; Kayafas, E.

doi:10.1109/tits.2006.880641

Cited by 597 publications

(259 citation statements)

References 33 publications

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“…A variety of approaches have been proposed to localize a license plate in captured video images (Al-Hmouz and Aboura, 2014). Some of the existing methods are morphological operations, edge detection, corner detection, sliding concentric windows (Anagnostopoulos et al, 2006), fuzzy logic (Chang et al, 2006 ), Hough transform (Duc et al,2005), neural networks (Kim et al 2000), Fourier transform (Acosta, 2004), adaptive boosting (AdaBoost) algorithm (Dlagnekov, 2004). Al-Hmouz and Aboura (2014) introduce a new approach of plate localization using a statistical analysis of Discrete Fourier Transform of the plate signal.…”

Section: License Plate Localizationmentioning

confidence: 99%

“…Given that it is a stochastic problem, one expects probability answers, if one adheres to the principle that probability is the only coherent way to address uncertainty (Lindley, 1987). Despite some attempts, such as the probabilistic transition trees (Eichelberger and Najarian, 2006), the only noticeable probability based research direction is that of the probabilistic neural networks, for example (Anagnostopoulos, Anagnostopoulos, Loumos and Kayafas, 2006). The probabilistic neural network (PNN) was developed by Donald Specht (1988) and provides a solution to classification problems using Bayesian classifiers and the Parzen Estimators.…”

Section: Probabilistic Optical Character Recognitionmentioning

confidence: 99%

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An Overview of Image Analysis Algorithms for License Plate Recognition

Aboura

Al‐Hmouz

2017

Organizacija

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Background and purpose:We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.

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Section: License Plate Localizationmentioning

confidence: 99%

Section: Probabilistic Optical Character Recognitionmentioning

confidence: 99%

An Overview of Image Analysis Algorithms for License Plate Recognition

Aboura

Al‐Hmouz

2017

Organizacija

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“…A suitable technique for the recognition of single font and fixed size characters is the pattern matching technique. The recognition process was based on the computation of the normalized cross correlation values for all the shifts of each character template over the sub-image containing the licence plate [7].…”

Section: Fig 1 Diagram Of Our Lpr Processmentioning

confidence: 99%

“…Numerous algorithms have previously been exploited such as, Hidden Markov Models (HMM) [3], Artificial Neural Networks (ANN) [4], Hausdorff Distance [5], Support Vector Machine [6] (SVM)-based character recognizer and template matching that leave a lot of room for improvements [7]. The focus of this paper is to investigate a character recognition technique using the Artificial Immune System (AIS) based CSA.…”

Section: Introductionmentioning

confidence: 99%

Licence Plate Character Recognition Using Artificial Immune Technique

Huang

Tawfik

Nagar

2008

Computational Science – ICCS 2008

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Abstract. This paper proposes the application of Artificial Immune Technique in Licence Plate Character Recognition (LPCR). The use of Clonal Selection Algorithm (CSA) is composed of two main stages: (1) dynamic training samples; and (2) a choice of the best antibodies based on the three main clonal operations of cloning, clonal mutation and clonal selection. Once memory cells are established it will output the classification results using Fuzzy K-Nearest Neighbor (KNN) approach. The performance of CSA is compared to the Back Propagation Neural Networks (BPNN) in solving a LPCR problem. The experimental results show that the Artificial Immune Technique has a favorable performance in terms of being more accurate and robust.

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“…All these features can be used alone or associatively. The most commonly used classification methods are template matching [3][4][5]9] and neural network [1,7,8,10]. The former is based on the difference between sample and template, and the latter is based on the generalization ability of the network.…”

Section: Introductionmentioning

confidence: 99%

Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition

Cheng

Bai

et al. 2015

Entropy

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Abstract:In this paper, a novel self-creating disk-cell-splitting (SCDCS) algorithm is proposed for training the radial wavelet neural network (RWNN) model. Combining with the least square (LS) method which determines the linear weight coefficients, SCDCS can create neurons adaptively on a disk according to the distribution of input data and learning goals. As a result, a disk map is made for input data as well as a RWNN model with proper architecture and parameters can be decided for the recognition task. The proposed SCDCS-LS based RWNN model is employed for the recognition of license plate characters. Compared to the classical radial-basis-function (RBF) network with K-means clustering and LS, the proposed model can make a better recognition performance even with fewer neurons.

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A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

Cited by 597 publications

References 33 publications

An Overview of Image Analysis Algorithms for License Plate Recognition

An Overview of Image Analysis Algorithms for License Plate Recognition

Licence Plate Character Recognition Using Artificial Immune Technique

Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition

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