• The KIPS Transactions:PartD
• /
• v.9D no.6
• /
• pp.1113-1118
• /
• 2002

In recognition system of the car license plate, the most important is to extract the image of the license plate from a car image. In this paper, we use ACL (Adaptive Color Luminance) algorithm to extract the license plate image from a car image. The ACL algorithm that uses color and luminance information of a car image is used to extract the image of the license plate. In this paper, color, luminance and other related information of a car image are used to extract the image of the license plate from that of a car. In this reason, we call it the ACL algorithm. The ACL algorithm uses color, luminance information and other related information of a license plate. These informations are avaliable to exact the image of the license plate. The rate of extracting the image of the license plate from a car is 97%. The experimental result of the ACL algorithm for the character region is 92%.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.92-98
• /
• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.3
• /
• pp.684-689
• /
• 2005

In this paper, we propose car license plate recognition using morphological information and an enhanced neural network. Morphological information on horizontal and vertical edges was used to extract the license plate from a car image. We used a contour tracking algorithm combined with the method of histogram and location information to extract individual characters in the extracted plate. The enhanced neural network is proposed for recognizing them, which has the method of combining the ART-1 and the supervised teaming method. The proposed method has applied to real world car images. The experimental results show that the proposed method has better the extraction rates than the methods with information of the thresholding, the RGB and the HSI, respectively. And the proposed neural network has better recognition performance than the conventional neural networks.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.01a
• /
• pp.339-342
• /
• 2014

현재 차량의 출입통제 및 주정차 단속 등이 차량 번호판 자동 인식 시스템을 통해 자동화 되고 있다. 본 논문은 촬영 각도에 따라 기울어지거나 왜곡된 번호판에 대해서도 잘 동작하는 번호판 영역 추출 알고리즘을 제안한다. 번호판의 배경과 문자의 밝기 대비가 커서 그 분포가 이원 양상을 보인다는 점을 이용하여 번호판의 중심부와 대략적인 후보 영역을 추출한다. 이후 허프 변환을 통하여 번호판의 네 모서리에 해당하는 직선을 검출한다. 이들 네 직선의 교점이 번호판의 꼭짓점이 된다. 네 꼭짓점의 좌표를 이용하여 왜곡된 번호판을 실제 번호판의 가로와 세로 비율에 맞는 정규화 된 모양으로 변환한다. 차량의 측면 1m~3m 사이의 다양한 거리에서 촬영한 이미지로 실험한 결과 일반적인 실외 조명 아래에서 차체의 색에 관계없이 번호판 영역 추출에 성공하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.6 no.2
• /
• pp.113-118
• /
• 2013

This study will suggest methods for a license plate recognition system that is suitable for license plate identification, separation of letters, and recognition of letters in order to recognize a licence plate efficiently. The suggested algorithm had tested a recognition system that onlyused backpropagation, a recognition system that used only SVM, and the suggested recognition system in order to prove efficiency. As a result, recognition rate had increased from the minimum 7.9% to the maximum12.2% as the case of using back propagation recognized the number platefor 87.9%, the case of using SVM for 91.4%, and the suggested had 98.6% of recognition rate.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.05c
• /
• pp.365-370
• /
• 2002

본 논문은 차량의 번호판 인식 시스템을 구축할 목적으로 번호판 추출을 컬러 정보를 이용하여 구현하였다. 본 실험은 특히 번호판 추출에 많은 장애 요인, 즉, 흐린날, 늦은 오후에 촬영된 차량, 번호판과 같은 색상을 가지는 차량, 그늘진 곳에 주차된 차량 그리고, 운행중인 차량에 대해서도 실험 대상으로 하였다. 그리고, 본 논문에서는 빠른 번호판 검출을 위하여 부분 영역에 대한 특정 픽셀을 추출하였고, 변형 모폴로지와 레이블링의 반복 적용을 통하여 번호판을 검출하며, 후처리를 통하여 보다 정확한 영역을 재보정 한다.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.9
• /
• pp.361-368
• /
• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.5
• /
• pp.1052-1061
• /
• 2003

In this paper, we propose the car plate recognition using SOM algorithm and describe the parking control system using the proposed car plate recognition. The recognition of car plate was investigated by means of the SOM algorithm. The morphological information of horizontal and vertical edges was used to extract a plate area from a car image. In addition, the 4-direction contour tracking algorithm was applied to extract the specific area, which includes characters from an extracted plate area. The extracted characteristic area was recognized by using the SOM algorithm. In this paper, 50 car images were tested. The extraction rate obtained by the proposed extraction method showed better results than that from the color information of RGB and HSI, respectively. And the car plate recognition using SOM algorithm was very efficient. We develop the parking control system using the proposed car plate recognition that shows performance improvement by the experimental results.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.6
• /
• pp.1686-1692
• /
• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.8
• /
• pp.1352-1357
• /
• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.