• International journal of advanced smart convergence
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• v.9 no.1
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.116-125
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• 2003

In this paper, we introduce a newly developed DSP module and Software which Is applicable to DTV-OCR and is designed to cancel the interference signal. In general, RF repeater has problems of system oscillation and signal Quality degradation due to feedback interference signal coming from transmit antenna. In this paper, we demonstrate newly developed DSP HW and SW module for cancelling the interference signal by investigating the field data measured through a RF repeater. Also, the structure and signal processing method for non-regenerative repeater system based on the newly developed DSP HW and SW module is illustrated as well.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1713-1723
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• 1999

Optical character recognition(OCR) might be the most plausible method in building database out of printed matters. This paper describes the points to be considered when one selects an OCR system in order to build database. Based on the considerations, we evaluated four commercial OCR systems, and chose one which shows the best recognition rate to build OCT-text database. The subject text, the KT-test collection, is a set of abstracts from proceedings of different printing quality, fonts, and formats. KT-test collection is also provided with typed text database. Recognition rate was calculated by comparing the recognition result with the typed text. No preprocessing such as learning and slant correction was applied to the recognition process in order to simulate a practical environment. The result shows 90.5% of character recognition rate over 970 abstracts. This recognition rate is still insufficient for practical use. The errors in OCR texts are different from those of manually typed texts. In this paper, we classify the errors in OCR texts for the further research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.523-529
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• 2021

In the era of the Fourth Industrial Revolution, technological changes are being applied in various fields. Automation digitization and data management are also in the field of bills. There are more than tens of thousands of forms of bills circulating in society and bill recognition is essential for automation, digitization and data management. Currently in order to manage various bills, OCR technology is used for character recognition. In this time, we can increase the accuracy, when firstly recognize the form of the bill and secondly recognize bills. In this paper, a logo that can be used as an index to classify the form of the bill was recognized as an object. At this time, since the size of the logo is smaller than that of the entire bill, FPN was used for Small Object Detection among deep learning technologies. As a result, it was possible to reduce resource waste and increase the accuracy of OCR recognition through the proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1329-1338
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• 2021

There are rarely effective methods to help visually impaired people when they want to know the expiration date of products excepted to only Braille. In this study, we developed an expiration date notification system based on YOLO and OCR for visually impaired people. The handicapped people can automatically know the expiration date of a specific product by using our system without the help of a caregiver, fast and accurately. The proposed system is worked by four different steps: (1) identification of a target product by scanning its barcode; (2) segmentation of an image area with the expiration date using YOLO; (3) classification of the expiration date by OCR: (4) notification of the expiration date by TTS. Our system showed an average classification accuracy of about 86.00% when blindfolded subjects used the proposed system in real-time. This result validates that the proposed system can be potentially used for visually impaired people.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.451-457
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• 2020

Format recognition and OCR techniques are widely used as methods for detecting and protecting personal information from electronic documents. However, due to the poor recognition rate of the OCR engine, personal information cannot be detected or false positives commonly occur. It also takes a long time to analyze a large amount of electronic documents. In this paper, we propose a method to improve the speed of image analysis of electronic documents, character recognition rate of the OCR engine, and detection rate of personal information by improving the existing method. The analysis speed was increased using the format recognition method while the analysis speed and character recognition rate of the OCR engine was improved by image correction. An algorithm for analyzing personal information from images was proposed to increase the reconnaissance rate of personal information. Through the experiments, 1755 image format recognition samples were analyzed in an average time of 0.24 seconds, which was 0.5 seconds higher than the conventional PAID system format recognition method, and the image recognition rate was 99%. The proposed method in this paper can be used in various fields such as public, telecommunications, finance, tourism, and security as a system to protect personal information in electronic documents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1358-1367
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• 1993

In this paper, OMR system based on image processing is developed which improve the performance of conventional OMR system based on line-scan method. Based on this OMR system, real-time OCR system which recognizes alphanumerics is also developed. We propose the OMCR system which recognize the mark and numerals at the same time. Besides, we improve the input system using constrained 7-segment type handwritten numeral instead of mark to solve the problem caused by miswriting the mark. In summary, we verified the reai-time recognition performance of developed OMCR system using application form for admission, answer sheet for college entrance examination and receipt sheet.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.38-38
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• 2009

Characteristics of the photonic bandgaps (PBGs) in two-dimensional photonic crystals (2D PCs) with a hexagonal lattice have theoretically studied using a finite difference time domain (FDTD) simulation. In this research, we propose a concept of optical coverage ratio (OCR) as a new structural parameter to determine the PBGs for E-polarized light. The OCR is an optically compensated filling factor. It is possible to normalize the PBGs of 2D PCs by introducing the OCR.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.704-710
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• 2016

In this paper, we proposed pole position detection system for providing exact location information to users. The proposed system consists of pole recognition part and pole number recognition part. Above all, exact pole recognition is carried out by PDD(Pole Detection Device). And recognition of pole number is performed by PID(Pole Inspection Device). Acquired image by using line scan camera is judged whether it is free bracket or not through image processing. When it is judged as free bracket, pole number image is acquired by OCR camera and recognized by OCR. By recognizing pole number, exact location information is provided to user.