• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
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• pp.213-217
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• 2003

이 논문에서는 MLP(Multilayer Perceptron)가 지닌 잡음 강건성에 대한 통계학적 분석을 하였다. 또한, MLP의 잡음 강건성을 향상시키기 위한 선형적 전처리 단계로써, ICA(independent component analysis)와 PCA(principle component analysis)를 고려하여, 이들이 지닌 잡음처리 효과를 분석한후, MLP와 접목시 나타나는 잡음 강건성의 향상 여부를 필기체 숫자 인식의 시뮬레이션으로 확인하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
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• pp.610-612
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• 2002

본 논문은 오프라인 필기체 숫자 인식을 위하여 SOG* 세선화와 방향 코드 생성만으로 전처리를 단순화하여 효율을 높이는 새로운 방법을 제안한다. 본 실험의 객관적 검증을 위해 Concordia 대학교 등의 여러기관의 필기체 숫자 데이터베이스에 대하여 실험한 결과 98.85% 이상의 인식률을 나타내어 단순한 전처리로 높은 인식률을 얻음으로써 효율성이 높음을 알 수 있다.

• ETRI Journal
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• 제16권1호
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• pp.35-43
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• 1994

In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.52-58
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• 1995

A shared-weight neural network that performed classification based on the features extracted with the fuzzy morphological operation is introduced. Learning rules for the structuring elements, degree of membership, and weighting factors are also precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of-art for this problem.

• 전기학회논문지
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• 제66권2호
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• pp.416-424
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• 2017

In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.681-684
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• 2021

In this paper, we study image clustering without labeling using machine learning techniques. We proposed an unsupervised machine learning technique to design an image clustering model that automatically categorizes images into groups. Our experiment focused on inception convolutional neural networks (inception V3) with k-mean methods to cluster images. For this, we collect the public datasets containing Food-K5, Flowers, Handwritten Digit, Cats-dogs, and our dataset Rice Germination, and the owner dataset Palm print. Our experiment can expand into three-part; First, format all the images to un-label and move to whole datasets. Second, load dataset into the inception V3 extraction image features and transferred to the k-mean cluster group hold on six classes. Lastly, evaluate modeling accuracy using the confusion matrix base on precision, recall, F1 to analyze. In this our methods, we can get the results as 1) Handwritten Digit (precision = 1.000, recall = 1.000, F1 = 1.00), 2) Food-K5 (precision = 0.975, recall = 0.945, F1 = 0.96), 3) Palm print (precision = 1.000, recall = 0.999, F1 = 1.00), 4) Cats-dogs (precision = 0.997, recall = 0.475, F1 = 0.64), 5) Flowers (precision = 0.610, recall = 0.982, F1 = 0.75), and our dataset 6) Rice Germination (precision = 0.997, recall = 0.943, F1 = 0.97). Our experiment showed that modeling could get an accuracy rate of 0.8908; the outcomes state that the proposed model is strongest enough to differentiate the different images and classify them into clusters.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권1호
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• pp.93-93
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• 1999

Researches on digit recognition have been conducted actively for a long time because the classes to recognize are much fewer than other character sets and because it is very likely thatthe digit recognition can be applied to many problems in real world, The recent studies on designingrecognition system with high performance are in progress with two different aspects. One is toconstruct a recognizer using several features at the same time, and the other is to use severalrecognizers. In this paper, we propose a multistage combination method to recognize the unconstrainedhandwritten numerals. The method is a two-stage combination method which uses multiplecombination methods at the same time unlike the existing methods with only one combination method.The recognizers are first combined by several combination methods of different classes simultaneously,and then the results of them are combined by another combination method to generate a final result.Five recognizers and eight combination methods are used in the proposed system. The experimentalresults showed that the recognition rates on CENPARMI and CEDAR data were 97.75% and 98.6%,respectively and the recognition performance could be improved as the process passed through stages,We could get the best performance by combining the combination methods of different classes, whichmeans there are a complementary relation among them, The proposed method can be considered asan extended version of the existing combination methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.260-267
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• 2015

In the past, we implemented a web-based smart education platform, but this is not efficient in a smart or mobile education environment. Therefore, in this paper, we propose an Android-based e-board smart platform for a smart or mobile education system. Here, we use Anoto digital pen- and dot pattern-based technologies. This Android-based smart education platform is efficient for a smart education environment. Further, we implement the hardware and software parts of the technologies, an Anoto-based trajectory recognition algorithm, and a probabilistic neural network for handwritten digit and hand gesture recognition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.162-164
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• 2004

This paper proposes a new classification system combining the adaptive feature weighting algorithm using the genetic algorithm and the modified KNN rule. GA is employed to choose the middle value of weights and weights of features for high performance of the system. The modified KNN rule is proposed to estimate the class of test pattern using adaptive feature space. Experiments with the unconstrained handwritten digit database of Concordia University in Canada are conducted to show the performance of the proposed method.

• 전자공학회논문지B
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• 제32B권6호
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• pp.922-931
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• 1995

In this paper, we propose a modified error function to improve the EBP(Error Back-Propagation) algorithm of Multi-Layer Perceptrons. Using the modified error function, the output node of MLP generates a strong error signal in the case that the output node is far from the desired value, and generates a weak error signal in the opposite case. This accelerates the learning speed of EBP algorothm in the initial stage and prevents overspecialization for training patterns in the final stage. The effectiveness of our modification is verified through the simulation of handwritten digit recognition.