• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.737-747
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• 2012

Recently, in order to support automatic meter reading for conventional metering devices, an image processing-based approach of recognizing the number meter data in the captured meter images has attracted many researchers' interests. Numerical character segmentation is a very critical process for successful recognition. In this paper, we propose an efficient numeric character segmentation method which can segment numeric characters well for any metering device types under diverse illumination environments. The proposed method consists of two consecutive stages; detection of number area containing all numbers as a tight ROI(Region of Interest) and segmentation of numerical characters in the ROI. Detection of tight ROI is achieved in two steps: extraction of rough ROI by utilizing horizontal line segments after illumination enhancement preprocessing, and making the rough ROI more tight through clipping utilizing vertical and horizontal projection about binarized ROI. Numerical character segmentation in the detected ROI is stably achieved in two processes of 'vertical segmentation of each number region' and 'number segmentation in the each vertical segmented number region'. Through the experiments about a homegrown meter image database containing various meter type images of low contrast, low intensity, shadow, and saturation, it is shown that the proposed numeric character segmentation method performs effectively well for any metering device types under diverse illumination environments.

• Research in Mathematical Education
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• v.14 no.1
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• pp.33-50
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• 2010

The mathematical thinking which transforms important mathematical content and developed into mathematical structure is a vital process in building up mathematical ability as mathematical knowledge based on structure. Such process based on students' recognition of mathematical concept. Developing mathematical thinking into mathematical structure happens when different cognitive units are connected and compressed to form schema of solution, which could happen through some guided problems. The effort of arithmetic approach in problem solving did not necessarily provide students the structure schema of solution. The using of equation to solve the problem is based on the schema of building equation, and is not necessary recognizing the structure of the solution, as the recognition of structure may be lost in the process of simplification of algebraic expressions, leaving only the final numeric answer of the problem.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.573-583
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• 2002

Pattern recognition process is made up of the feature extraction in the pre-processing, the pattern clustering by training and the recognition process. This paper presents the F-LVQ (Fuzzy Learning Vector Quantization) pattern classification model which is optimized by the fuzzy membership function for the OCR(Optical Character Recognition) system. We trained 220 numeric patterns of 22 Hangul and English fonts and tested 4840 patterns whose forms are changed variously. As a result of this experiment, it is proved that the proposed model is more effective and robust than other typical LVQ models.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.304-308
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• 2010

In this paper, we propose a new method of recognizing the vehicle license plate using color space, morphological characteristics and ART2 algorithm. Morphological characteristics of old and/or new style vehicle license plate among the candidate regions are applied to remove noise areas using 8-directional contour tracking algorithm, then follow by the extraction of vehicle plate. From the extracted license plate area, plate morphological characteristics of each region are removed. After that, labeling algorithm to extract the individual characters are then combined. The classified individual character and numeric codes are applied to the ART2 algorithm for the learning and recognition. In order to evaluate the performance of our proposed extraction and recognition of vehicle license method, we have run experiments on 100 green plates and white plates. Experimental results shown that the proposed license plate extraction and recognition method was effective.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.1-7
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• 2003

This study is a research on character recognition based on image processing, using only color tape to mark guidelines instead of magnetic tape or electric wire used by existing AGV. AGV must follow given courses, and stop recognizing signs such as marks and numbers that indicate destinations. In this study. marks to stop AGV employed blue characters of the same font and size as those of number plates. Yellow driving lines and blue numeric characters were marked in corridors. AGV ran ing the characteristics of colors and detecting lines, and temporarily stopped recognizing numbers of 100％ through DP pattern matching. This study presented the image processing technique and the result of operating AGV.

• 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%.

• Journal of Drive and Control
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• v.20 no.4
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• pp.92-99
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• 2023

Autonomous vehicles should recognize and respond to the specified speed to drive in compliance with regulations. To recognize the specified speed, the most representative method is to read the numbers of the signs by recognizing the speed signs in the front camera image. This study proposes a method that utilizes YOLO-Labeling-Labeling-EfficientNet. The sign box is first recognized with YOLO, and the numeric digit is extracted according to the pixel value from the recognized box through two labeling stages. After that, the number of each digit is recognized using EfficientNet (CNN) learned with the virtual environment dataset produced directly. In addition, we estimated the depth of information from the height value of the recognized sign through regression analysis. We verified the proposed algorithm using the virtual racing environment and GTSRB, and proved its real-time performance and efficient recognition performance.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.446-448
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• 1999

본 논문에서는 일상 생활에서 쉽게 접할 수 있는 지로(Giro) 서식 상에 있는 인쇄체 숫자열 인식 방법으로서 템플릿 매칭 방법에 대해 설명한다. 지로 서식 문서 상의 인쇄체 숫자는 인쇄시의 오류로 인하여 숫자의 굵기나 높이, 그리고 폭이 다를 수는 있지만 기본적으로 폰트의 유형이 한가지라는 것과 나타날 수 있는 오류의 유형이 몇 가지로 제한되어 있다는 특징을 갖는다. 따라서 이러한 데이터 특징을 효율적으로 수용할 수 있도록 템플릿을 정의한 후 매칭 방법을 통해 숫자를 인식하는 템플릿 매칭 방법에 대해 설명한다. 실험 결과 비교적 간단한 방법을 이용하더라고 인쇄체 숫자열을 효율적으로 인식할 수 있었다.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1273-1276
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• 1998

The HMM-Net is an architecture for a neural network that implements a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood (ML), maximum mutual information (MMI), and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM-Net classifiers using hybrid criteria, ML/MMSE and MMI/MMSE, and report the results of an experimental study comparing the performance of HMM-Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.933-935
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• 1998

The HMM-Net is an architecture for a neural network that implements a hidden markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood(ML) and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM_Net classifiers using a ML-MMSE hybrid criterion and report the results of an experimental study comparing the performance of HMM_Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the repects of learning and recognition rates.