• Journal of Information Processing Systems
• /
• v.12 no.4
• /
• pp.661-680
• /
• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.12
• /
• pp.1114-1125
• /
• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.1
• /
• pp.77-86
• /
• 2001

With the growth of the EC(Electronic Commerce), Buying and selling software through the internet are expanded. Among the ESD(Electronic Software Distribution) methods, Buy-first method and TrY-before-bu1 method can not solve the illegal copy problem. Recently developed EL(Electronic License) model solve the illegal copy problem by separating the software and license. But this method also can not support various ways for payment. In this paper we propose the software license management system that is a newly form like ESD model. This system proposes NL(New License) to support various payment methods and SC(Software Charge) to insure that a seller takes the software price. Agent of the proposed system offers scalability to other systems and illegal copy protection function bv managing NL and SC.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.5
• /
• pp.747-753
• /
• 2018

In the license-plate recognition systems for automobile black Image, the license-plate image frequently has a shadow due to outdoor environments which are frequently changing. Such a shadow makes unpredictable errors in the segmentation process of individual characters and numbers of the license plate image, and reduces the overall recognition rate. In this paper, to improve the recognition rate in these circumstance, a license-plate image binarization algorithm is proposed removing the shadow effectively. The propose algorithm splits the license-plate image into the regions with the shadow and without. To find out the boundary of two regions, the algorithm estimates the curve for shadow boundary using the least-squares method. The simulation is performed for the license-plate image having its shadow, and the results show much higher recognition rate than the previous algorithm.

• Journal of Korea Multimedia Society
• /
• v.25 no.4
• /
• pp.568-574
• /
• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• The Journal of Information Technology
• /
• v.3 no.1
• /
• pp.89-97
• /
• 2000

Electronic commerce on the network requires a new payment system, electronic cash that carries digitalized information of cash. The new system, however, demands the security and privacy because electronic cash is different from real cash in that it can be easily duplicated, forged, or traced. Therefore, electronic cash system should be guaranteed security and privacy to be used as a new payment mechanism with assurance. First, this paper analyzes the existing electronic cash system involving electronic coins, divisible electronic bills, and electronic license. Then the paper proposes a new electronic cash system which claims to be more efficient in the amount of communication and computation. Finally, this study attempts to solve the problem of electronic license.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.3
• /
• pp.166-171
• /
• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

• IE interfaces
• /
• v.13 no.1
• /
• pp.133-140
• /
• 2000

As the internet dramatically grows, internet electronic commerce is being increased. But, as products distributed with ESD(Electronic Software Distribution) are in the form of electronic file, it is difficult to prohibit illegal copy of products. Therefore, new technology is needed to distinguish legal products from illegal ones. In this paper, we present the ESD's requirements, characteristics and a new dynamic license management technology to prevent illegal copy.

• Journal of the Korean Society for Library and Information Science
• /
• v.38 no.1
• /
• pp.199-228
• /
• 2004

The purpose of the study is to develop a license model which could be used as guidelines during the process of contracting for the use of electronic journals in the academic libraries. The study was done through the following 4 steps. First, through the analysis of previous related literature, the study examined the licensing principles, the structure of the license, the relationship between copyright and contracts, and the development of license models in the library. Secondly, through the analysis of the basic structure of 5 representative license models abroad and 2 domestic licenses, the study established the basic structure of license model. Thirdly, a questionnaire was conducted on library licensee and the vendor licensor in order to find out the present states and problems of library contracting and their opinion on the core clauses of the contract. Lastly, having based on the results of the investigations mentioned above, the study finally developed the license model.

• Journal of Korea Multimedia Society
• /
• v.20 no.8
• /
• pp.1166-1174
• /
• 2017

In general, the detection of the vehicle license plate is a previous step of license plate recognition and has been actively studied for several decades. In this paper, we propose an algorithm to detect a license plate area of a moving vehicle from a video captured by a fixed camera installed on the road using the Convolution Neural Network (CNN) technology. First, license plate images and non-license plate images are applied to a previously learned CNN model (AlexNet) to extract and classify features. Then, after detecting the moving vehicle in the video, CNN detects the license plate area by comparing the features of the license plate region with the features of the license plate area. Experimental result shows relatively good performance in various environments such as incomplete lighting, noise due to rain, and low resolution. In addition, to protect personal information this proposed system can also be used independently to detect the license plate area and hide that area to secure the public's personal information.