License Plate Recognition using Multi-cluster and Multilayer Neural Networks

Abdullah, Siti Norul Huda Sheikh; Khalid, Marzuki; Yusof, Rubiyah; Omar, Khairuddin

doi:10.1109/ictta.2006.1684663

Cited by 25 publications

(13 citation statements)

References 10 publications

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“…In the following table, our method is compared with SVM method represented in [20] and traditional template matching mentioned in [21]. Some of the statistics used are reported in [22].…”

Section: Experiments Results and Analysismentioning

confidence: 99%

License Plate Recognition algorithm based on derived kernel

Zhang

Tang

2012

2012 International Conference on Wavelet Analysis and Pattern Recognition

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License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management. In this paper, an updated algorithm is applied into the vehicle license plate identification, which is mainly based on derived kernel through visual cortex. With the two-layer derived kernel on neural response and first nearest classification method applied to character and numeral recognition, the errors caused by blur and noise can be reduced, so that the recognition accuracy can be improved on a certain of complex and unclear circumstance.

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“…In the following table, our method is compared with SVM method represented in [20] and traditional template matching mentioned in [21]. Some of the statistics used are reported in [22].…”

Section: Experiments Results and Analysismentioning

confidence: 99%

License Plate Recognition algorithm based on derived kernel

Zhang

Tang

2012

2012 International Conference on Wavelet Analysis and Pattern Recognition

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“…The previous works have rate of accuracy: rate of accuracy of [9] is 91%, rate of accuracy of [10] is 94.0%, rate of accuracy of [11] is 93.1%, rate of accuracy of [12] is 87.22%, rate of accuracy of [13] is 88.3%, rate of accuracy of [14] is 82.5%, some experiment examples are shown in the Fig.11. VI.…”

Section: Using Network For Character Recognitionmentioning

confidence: 99%

“…In the segmentation work, there are also some techniques for this work such as based on edge detection, color model transform [4], an intelligent framework that outlines character by various illumination effects [5], etc. In the ALPR system, there are many techniques such as based on decision trees [6], Hough transform and Hidden Markov Model [7], Support Vector Machine (SVM) [8], multi-cluster and Multilayer Neural Networks [9], Least Squares Support Vector Machines (LS-SVM) [10], template-matching operators [11], Fuzzy Multilayer Neural Network [12], Radial Basis Function Neural Networks (RBFNN) [13], sliding concentric windows and histogram [14], extension theory [15]. But, most of these methods work under controlled conditions, their image data were not collected in Vietnam.…”

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confidence: 99%

An Improved Method for Vietnam License Plate Location, Segmentation and Recognition

Miao

Wang

et al. 2011

2011 International Conference on Computational and Information Sciences

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The Automatic License Plate Recognition (ALPR) is very important in the Intelligent Transportation System (ITS).In this paper we proposed an improved ALPR algorithm for Vietnam license plates (LP), which consists of three main modules: license plate location (LPL), character segmentation, character recognition. In the location work, we have improved algorithm based on edge detection, image subtraction, mathematic morphology to locate LP region, which considered removing noise. In the segmentation work, we have improved algorithm to get the segments in the LP by the peak-to-valley method in order to segment in digit images getting the two bounds of the each digit according to the statistical parameter. In the recognition work, we have used a Multi Layer Perceptron (MLP) neural network and back-propagation (BP) algorithm to recognize characters & numbers of the Vietnam LP, we used two networks for characters & numbers training with noises, in which the computing time and accuracy is improved.Our approach is more effective than some of the existing methods earlier and satisfied for Vietnam LP. We have been implemented on 600 images taken from actual scenes, different background such as light conditions (night and day), angles, illumination, size and type, colors, reflected light, dynamic conditions. The efficiency of the proposed approach is improved and average rate of accuracy of the one-row LP is 96.93%, two-row LP is 95.82%, higher than most of previous works.

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“…Now there are many way to recognize characters [5][6][7][8][9][10]. Some people use BP network [10].…”

Section: Recognition Of Number and Letter Charactermentioning

confidence: 99%

An effective method for number and letter character recognition

Xie

2008

2008 11th IEEE International Conference on Communication Technology

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An effective algorithm for number and letter character recognition is proposed in this paper. Our algorithm employs template matching, but it unlike traditional template matching method using the original pixel value to match. Our algorithm draws some features from the original image, and then obtains an eigenvector of 192 dimensions. Before drawing features, the image is disposed using math morphologic algorithm. And then measures the Euclidean distance between the sample vector and the template vector. Then we can obtain the result of recognition. A larger number of experiments prove that this algorithm own high performance and robustness. It can recognize characters which have high similarities, for example, 8 , B , R ,O and Q. This algorithm also tolerates the slightly tilt of the image. The recognition rate of this algorithm is 99.25%.

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License Plate Recognition using Multi-cluster and Multilayer Neural Networks

Cited by 25 publications

References 10 publications

License Plate Recognition algorithm based on derived kernel

License Plate Recognition algorithm based on derived kernel

An Improved Method for Vietnam License Plate Location, Segmentation and Recognition

An effective method for number and letter character recognition

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