• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.269-276
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• 2018

OCR(Optical Character Recognition) using computers has been developed for 20 years and applied to various fields such as parking management based on the recognition of license plates of cars. This technology was also used in the development of our smart OCR-based household ledger. In order to improve filling the purchase history into a smartphone based household account book, we can take pictures of receipts with the smarphone camera and automatically organize the purchase list. In this process, the recognition rate of the characters of the receipt image is not high enough with OCR technology. We could improve the rate by applying the image processing technology and adjusting the contrast of the receipt image. The rate improved from 89% to 92.5%.

• Journal of IKEEE
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• v.22 no.4
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• pp.1115-1122
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• 2018

In this paper, we analyze the performance degradation of previous 1-Bit convolutional neural network method and introduce ways to mitigate it. Previous work applies 32-Bit operation to first and last layers. But our method applies 32-Bit operation to second layer too. We also show that nonlinear activation function can be removed after binarizing inputs and weights. In order to verify the method proposed in this paper, we experiment the object detection neural network for korean license plate detection. Our method results in 96.1% accuracy, but the existing method results in 74% accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.227-228
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• 2014

In this paper, a license plate investigation which is one of parking usage is performed. We survey research that should be given time interval using the camera without the observer. We assigned the investigation time interval to each of to each of the parking car, average parking duration is determined. Parking turnover, the number of vehicles of parking per hour per parking surface, is examined.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.97-105
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• 2021

Recently, with the development of IT technology, unmanned systems are being introduced in many industrial fields, and one of the most important factors for introducing unmanned systems in the automobile field is vehicle licence plate recognition(VLPR). The existing VLPR algorithms are configured to use image processing for a specific type of license plate to divide individual areas of a character within the plate to recognize each character. However, as the number of Korean vehicle license plates increases, the law is amended, there are old-fashioned license plates, new license plates, and different types of plates are used for each type of vehicle. Therefore, it is necessary to update the VLPR system every time, which incurs costs. In this paper, we use an object detection algorithm to detect character regardless of the format of the vehicle license plate, and apply a handwritten Hangul recognition(HHR) algorithm to enhance the recognition accuracy of a single Hangul character, which is called a Hangul unit. Since Hangul unit is recognized by combining initial consonant, medial vowel and final consonant, so it is possible to use other Hangul units in addition to the 40 Hangul units used for the Korean vehicle license plate.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.391-396
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• 2019

Researches for image processing have been actively progress due to the development of various hardware. For example, in order to prevent various types of crime by a vehicle, there is a method of detecting the location of a criminal vehicle using the existing CCTV in real time. However, certain types of systems and high-performance system requirements make it difficult to apply to existing equipment. In this paper proposes a search algorithm that construct a file database of Korean standard license plate information so that specific vehicles can be quickly searched using existing equipment. In order to evaluate the performance of the file database and the search algorithm proposed in this paper, we set up the search targets at various locations and the results showed that the search algorithm could always check the information by searching the vehicle within a certain time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.90-100
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• 1993

A finite element formulation of vibration control of a laminated plate with piezoelectric sensor/ actuators is presented. Classical lamination theory with the induced strain actuation and Hamilton's principle are used to formulate the equations of motion of the system. The total charge developed on the sensor layer is calculated from the direct piezoelectric equation. The equations of motion and the total charge are discretized with 4 node, 12 degrees of freedom quadrilateral plate bending elements with one electrical degree of freedom. The mass and stiffness of the piezoelectric layer are introduced by treating them as another layer in laminated plate. Piezoelectric sensor/actuators are distributed, but discrete due to the geometry of electrodes. By defining an i.d. number of electrode for each element, modelling of electrodes with variable geometry can be achieved. The static response of a piezoelectric bimorph beam to electrical loading and sensor voltage to given displacement are calculated. For a laminated plate under the negative velocity feedback control, the direct time response by the Newmark-.betha. method and damped frequencies and modal damping ratios by modal state space analysis are derived.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.2
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• pp.185-203
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• 2013

This study was to suggest the new rules for revision of KCR4 by comparing between ISBD consolidated edition and KCR4. The study methods was to compare the rules in each element after mapping the description elements in each area of ISBD and KCR4. Resultingly, first, content forms and media types must be included for describing resource types. Second, it is needed for rules about the common title and the dependent title. Third, it is needed for rules about "parallel" such as parallel title, parallel other title information, parallel statement of responsibility relating to title, parallel edition statement, parallel statement of responsibility relating to edition, parallel numbering system, parallel place of publication, production and distribution, et. al. Fourth, the rules about material or type of resource specific area must be regulated in terms of the contents of the resource. Fifth, the home country principle must be not applied in describing the place of publication, production and distribution for the consistency. Sixth, it is needed to regulate the extent, other physical details, dimensions, and accompanying material statement for all materials instead of the material description according to material types. Seventh, rule number of notes must be agreed to number of main rules. Eighth, it is needed for detailed rules about resource identifier. This study might be contributed to revise the KCR4.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.141-143
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• 2022

This paper presented a new technology to identify traffic violations of motorbike riders by detecting the traffic signal using You Only Look Once (YOLO) object detection. The hardware module that is mounted on the front of the motorbike consists of Raspberry Pi with a camera to run the YOLO object detection, a GPS module to acquire the motorcycle's coordinate, and a LoRa communication module to send the data to a cloud DB. The main goal of the software is to determine whether a motorbike has violated a traffic signal. This paper proposes a function to recognize the red traffic signal colour with its movement inside the camera angle and determine that the traffic signal violation happens if the traffic signal is moving to the right direction (the rider turns left) or moving to the top direction (the riders goes straight). Furthermore, if a motorbike rider is violated the signal, the rider's personal information (name, mobile phone number, etc), the snapshot of the violation situation, rider's location, and date/time will be sent to a cloud DB. The violation information will be delivered to the driver's smartphone as a push notification and the local police station to be used for issuing violation tickets, which is expected to prevent motorbike riders from violating traffic signals.