• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.1-7
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• 2007

In order to recognize a vehicle license plate, the region of the license plate should be extracted from a vehicle image. Then, character region should be separated from the background image and characters are recognized using some neural networks with selected feature vectors. Of course, choice of feature vectors which serve as the basis of the character recognition has an important effect on recognition result as well as reduction of data amount. In this paper, we propose a novel feature extraction method in which number images are decomposed into linear combination of eigennumbers and show the validity of this method by applying to the recognition of numeric characters in license plates. The experimental results show the recognition rate of 95.3% for about 500 vehicle images with multi-layer perceptron neural network in the eigennumber space. Compared with the conventional mesh feature, it shows a better recognition rate by 5%.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.511-520
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• 2016

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.493-496
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• 2005

In this paper, we propose a new approach to detect candidate area of vehicle license plate using compounded color and vertical edge information it's own. Also, we propose a verification course, to compressed image generated by Fast DCT, using SVM to increase accuracy of extracted vechicle license plate area. Proposed method is consider that vehicle's position, become a object of it's license plate recognition, has various angle, scale and include enough environment informations. As a experimental results, proposed method shows a superior performance compared with the case that not includes verification course using SVM.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.270-274
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• 2015

We suggest that HSV Color Space may be used to detect a vehicle detecting system at nighttime. It is essential that a licence plate should be extracted when a vehicle is under surveillance. To do so, a licence plate may be enlarged to certain size after the aimed vehicle is taken picture from a distance by using Pan-Tilt-Zoom Camera. Either Mean-Shift or Optical Flow Algorithm is generally used for the purpose of a vehicle detection and trace, even though those algorithms have tendency to have difficulty in detection and trace a vehicle at night. By utilizing the fact that a headlight or taillight of a vehicle stands out when an input image is converted in to HSV Color Space, we are able to achieve improvement on those algorithms for the vehicle detection and trace. In this paper, we have shown that at night, the suggested method is efficient enough to detect a vehicle 93.9% from the front and 97.7% from the back.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.9-17
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• 2009

License plate recognition is an active research area. but few comparative studies on license plate binarization have been conducted. Many related researchers have experienced similar trial and error for finding an effective binarization method. To reduce this trial and error, this study implemented some binarization methods and quantitatively compared the performance of the methods. The performance evaluation consists of a low level measure and a high level measure, so it can evaluate not only the quality of binarized image itself but also the usefulness of the result. The performance evaluation was separately performed with three groups of images so as to understand the properties of the binarization methods. Experimental results show that the quality of binarization is more dependent on the evenness of illumination than the intensity of illumination. The Otsu's method has acquired the most effective performance in the group of even illumination images and the Niblack's method with parameter correction has shown the best quality in the group of uneven illumination images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.160-164
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• 2005

The process of recognizing a vehicle involves detection of the vehicle, recognition of the vehicle model, and identification of the vehicle. The process of vehicle identification involves identification of the vehicle itself, such as by recognition of the license plate on the vehicle. In this paper the method involves the use of a beam splitter to divide incident rays into two directions, a transmitted beam and a reflected beam of different light intensities, and synthesizing two captured images using CCD devices from each beam, thus producing fluctuation-free images of a wide dynamic range even when the subject is moving. A prototype license plate recognition system was also developed using the experimental sensing device. The system achieved a 98.7% recognition rate on 466 images of moving vehicles, which demonstrates its effectiveness as a license plate recognition system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.453-459
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• 2015

In this paper, we propose a new digit recognition method based on opened enclosure. In the proposed method, each digit is divided into two parts, an upper part and a lower part, which are based on a cutting line that is modified depending on the number of intersection points. In the simulation, the performance evaluation through the data acquisition and application of the proposed algorithm was carried out and the result was presented.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.780-785
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• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.568-570
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• 2002

최근, 분류기 쪽에서는 모듈라 학습을 이용한 방법들에 대해서 상당한 관심이 모아지고 있다. 모듈라 학습 방법은 divide and conquer 개념에 바탕을 두고 있기 때문에 복잡한 문제에 대해서 학습 질 측면이나 학습 속도 면에서 단일 분류기에 비해 좋은 결과들을 나타내고 있다. 인공신경망을 이용한 분류 방법 쪽에서도 이러한 연구들이 이루어지고 있다. 본 논문에서는 번호판 인식을 위한 간단한 형태의 모듈라 신경망을 제안하고 이의 성능을 평가하였다. 실험 결과, 일반적인 차량 번호판의 영상에서 성공적인 결과를 보였으며, 잡음에 의한 훼손된 번호판도 좋은 인식 결과를 보였다. 또한 인식률 측면 뿐만 아니라 학습 속도 면에서도 상당한 이득이 있었다.