• Korean Management Science Review
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• v.13 no.2
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• pp.1-11
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• 1996

Two different methods to extract the license plate area of a vehicle have been used for automatic recognition purposes. One method is with a color vision system and the other is with an edge detecting operator. The system with color vision has some problems if the colors of license plate and vehicle's body are similar. The various plate colors in Korea also drops the system performance. The edge detecting operator also has a problem for a real time processing since it performs on all pixels of the scene. In this paper a possible method using gray level vision system and available pre-known information of license plates is suggested. The suggested procedure searches the lower boundary of the plate by counting high contrast points between one and near pixel from the bottom line of the scene. It finds the upper boundary from the bottom line by adding number plate height after finding the lower boundary. The left and right boundaries are found by similar processes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2019-2026
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• 2001

In this paper, we proposed the car license plate extraction and recognition algorithm using both the dynamical thresholding method and the kohonen algorithm. In general, the areas of car license plate in the car images have distinguishing characteristics, such as the differences in intensity between the areas of characters and the background of the plates, the fixed ratio of width to height of the plates, and the higher dynamical thresholded density rate 7han the other areas, etc. Taking advantage of the characteristics, the thresholded images were created from the original images, and also the density rates were computed. A candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained from the car license plate. The contour tracking method by utilizing the Kohonen algorithm was applied to extract the specific area which included characters and numbers from an extracted plate area. The characters and numbers of the license place were recognized by using Kohonen algorithm. Kohonen algorithm was very effective o? suppressing noises scattered around the contour. In this study, 80 car images were tested. The result indicate that we proposed is superior in performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.113-123
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• 2012

Recently, various attempts have been made to apply Intelligent Transportation System under various environments and conditions. Consequently, an accurate license plate recognition regardless of vehicle location and viewing angle is required. In this paper, we propose a novel license plate recognition system which exploits a) the format of license plates to remove false candidates of license plates and to extract characters in license plates and b) the characteristics of Hangul for accurate character recognition. In order to eliminate false candidates of license plates, the proposed method first aligns the candidates of license plates horizontally, and compares the position and the shape of objects in each candidate with the prior information of license plates provided by Korean Ministry of Construction & Transportation. The prior information such as aspect ratio, background color, projection image is also used to extract characters in license plates accurately applying an improved local binarization considering luminance variation of license plates. In case of recognizing Hangul in license plates, they are initially grouped according to their shape similarity. Then a super-class method, a hierarchical analysis based on key feature points is applied to recognize Hangul accurately. The proposed method was verified with high recognition rate regardless of background image, which eventually proves that the proposed LPR system has high performance regardless of the vehicle location or viewing angle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5522-5540
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• 2018

Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.61-71
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• 2009

Vehicle license plate is the only way to check the registrated information of a vehicle. Many works have been devoted to the vision system of recognizing the license plate, which has been widely used to control an illegal parking. However, it is difficult to correctly segment characters on the license plate since an illumination is affected by a weather change and a neighboring obstacles. This paper proposes a robust method of segmenting the character of the license plate on irregular illumination condition. The proposed method enhance the contrast of license plate images using the Chi-Square probability density function. For segmenting characters on the license plate, binary images with the high quality are gained by applying the adaptive threshold. Preprocessing and labeling algorithm are used to eliminate noises existing during the whole segmentation process. Finally, profiling method is applied to segment characters on license plate from binary images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.909-919
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• 2014

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of $720{\times}576$ show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.41-44
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• 2015

본 논문에서는 안드로이드 기기를 활용하여 차량의 번호판을 인식하는 시스템을 제안한다. 이 시스템은 안드로이드 기기로 촬영한 차량의 이미지를 이용하여 번호판을 인식한다. 촬영한 이미지에서 번호판 영역을 추출한 후 번호판 영역 내에서 각각의 문자를 개별 추출한다. 추출된 각각의 문자에 대하여 세선화를 수행하고 세선화 후 얻은 이미지를 신경망의 입력으로 이용하여 최종적으로 개별의 문자를 인식하고 결과를 안드로이드 기기에 출력한다. 안드로이드 기기를 이용하여 바로 번호판을 인식할 수 있기 때문에 시, 공간에 대한 제약이 없으며 신경망을 사용하기 때문에 기존의 문자 인식 방법보다 우수한 인식률을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.652-655
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• 2012

With the development of IT and digital camera technology, diverse applications of image processing services are becoming available. In this paper, in order to ultimately automate parking management system, we designed and implemented a system for recognizing vehicle license plates that show vehicles' unique numbers by using EmguCV which shows optimized performance on Intel-based environment. We also implemented UI for administrators to easily manage the entire system by utilizing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.203-207
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• 2006

Nowadays, Cars are continuing to grow at an alarming rate but they also cause many problems such as traffic accident, pollutions and so on. One of the most effective methods that prevent traffic accidents is the use of traffic monitoring systems, which are already widely used in many countries. The monitoring system is beginning to be used in domestic recently. An intelligent monitoring system generates photo images of cars as well as identifies cars by recognizing their plates. That is, the system automatically recognizes characters of vehicle plates. An automatic vehicle plate recognition consists of two main module: a vehicle plate locating module and a vehicle plate number identification module. We study for a vehicle plate number identification module in this paper. We use image preprocessing, feature extraction, multi-layer neural networks for recognizing characters of vehicle plates and we present a feature-comparison method for improving the performance of vehicle plate number identification module. In the experiment on identifying vehicle plate number, 300 images taken from various scenes were used. Of which, 8 images have been failed to identify vehicle plate number and the overall rate of success for our vehicle plate recognition algorithm is 98%.