• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.125-130
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• 2004

This paper proposes a method of the car license plate recognition from digital camera image. Lots of technology advancement has been accomplished for the least several years. The key issue for recognition rate improvement has been the extraction of correct area on the plate. In the previous studies, the information from an edge or an color on a plate hasn't been used but some declination also taken into account in most cases due to the difficulty of area extraction on a tilted plate The proposed method focuses on transforming a slant plate image to the normalized form to be recognized. It shows good robustness on situations defined by a variety of locations, slants and heights of the license plate, because it detects the edge of license plate by using both the color information and linear regression method. The computer simulation shows that the proposed method records 92％ detection rates of license plate and can recognize characters of slant plate with about 50 degrees.

• Journal of IKEEE
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• v.15 no.4
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• pp.274-280
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• 2011

The implementation of the recognition system of a vehicle license plate and the Linux OS environment which is built in SoC Embedded system and its test result are presented in this paper. In order to recognize a vehicle license plate, each character has to be extracted from the whole image of a license plate and the extracted image is revised for the template matching. Labeling technique and numerical features are used to detect the vehicle license plate. Each character in the license plate has coordinates. The extracted image is revised by comparison of the numerical coordinates and recognized through template matching method. The experimental results show that the license plate detection rate is 96%, and a character recognition rate is 73%, and a number recognition rate is 97% for about 300 license plate images. The average time of the recognition in the embedded board is 0.66 sec.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.555-562
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• 2021

In this paper, we propose a sorting method for extracting accurate license plate information, which is currently used in Korea, after detecting objects using YOLO. We propose sorting methods for the five types of vehicle license plates managed by the Ministry of Land, Infrastructure and Transport by classifying the plates with the number of lines, Korean characters, and numbers. The results of experiments with 5 license plates show that the proposed algorithm identifies all license plate types and information by focusing on the object with high reliability score in the result label file presented by YOLO and deleting unnecessary object information. The proposed method will be applicable to all systems that recognize license plates.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.9-16
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• 2012

The research of vehicle license plate recognition has been widely studied for the smart city project. The license plate recognition can be hard due to the geometric distortion and the image quality degradation in case of capturing the driving car image at CCTV without trigger signal on the road. In this paper, the high performance vehicle license plate recognition system using edge-based segment image is introduced which is robust in the geometric distortion and the image quality degradation according to non-trigger signal. The experimental results of the proposed real time license plate recognition algorithm which is implemented at the CCTV on the road show that the plate detection rate was 97.5% and the overall character recognition rate of the detected plates was 99.3% in a day average 1,535 vehicles for a week operation.

• 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.

• Journal of IKEEE
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• v.23 no.4
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• pp.1337-1342
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• 2019

The Automatic Korean license plate recognition (AKLPR) is used in many fields. For many applications, high recognition rate and fast processing speed of ALPR are important. Recent advances in deep learning have improved the accuracy and speed of object detection and recognition, and CNN (Convolutional Neural Network) has been applied to ALPR. The ALPR is divided into the stage of detecting the LP region and the stage of detecting and recognizing the character in the LP region, and each step is implemented with separate CNN. In this paper, we propose a single stage CNN architecture to recognize license plate characters at high speed while keeping high recognition rate.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.7-11
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• 2023

In this study, we presented a method to recognize vehicle license plates using CNN techniques. A vehicle plate is normally used for the official identification purposes by the authorities. Most regular Optical Character Recognition (OCR) techniques perform well in recognizing printed characters on documents but cannot make out the registration number on the number plates. Besides, the existing approaches to plate number detection require that the vehicle is stationary and not in motion. To address these challenges to number plate detection we make the following contributions. We create a database of captured vehicle number plate's images and recognize the number plate character using Convolutional Neural Networks. The results of this study can be usefully used in parking management systems and enforcement cameras.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.845-850
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• 2008

In this paper, we propose a license plate locating algorithm by using SVM. Tipically, the features regarding license plate format include height-to-width ratio, color, and spatial frequency. The method is dived into three steps which are image acquisition, detecting license plate candidate regions, verifying the license plate accurately. In the course of detecting license plate candidate regions, color filtering and edge detecting are performed to detect candidate regions, and then verify candidate region using Support Vector Machines(SVM) with DCT coefficients of candidates. It is possible to perform reliable license plate location bemuse we can protect false detection through these verification process. We validate our approach with experimental results.

• Journal of IKEEE
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• v.23 no.2
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• pp.642-651
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• 2019

One of the most important research topics on intelligent transportation systems in recent years is detecting and recognizing a license plate. The license plate has a unique identification data on vehicle information. The existing vehicle traffic control system is based on a stop and uses a loop coil as a method of vehicle entrance/exit recognition. The method has the disadvantage of causing traffic jams and rising maintenance costs. We propose to exploit differential image of camera background instead of loop coil as an entrance/exit recognition method of vehicles. After entrance/exit recognition, we detect the candidate images of license plate using the morphological characteristics. The license plate can finally be detected using SVM(Support Vector Machine). Letter and numbers of the detected license plate are recognized using CNN(Convolutional Neural Network). The experimental results show that the proposed algorithm has a higher recognition rate than the existing license plate recognition algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.