• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.11-14
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• 1995

A Fuzzy Classifier which deals with very confusing objects is proposed. Naturally this classifier heavily relies on the nulti-feature decision-making procedure. For a simple example, this classifier is applied to the recognition of confusing handwritten numerals 4,6 and 9 The characteristic variables used in this paper are the existence of a loop and the relative location of the starting or ending points(SEP). Thus each sample of handwritten numerals 4, 6 and 9 is classified in one of the 6 groups which are divided according to the sample structure. Each group has its own classifying rules. Also the method of rule-generation using genetic algorithms in each group is proposed.

• The Journal of Information Technology
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• v.9 no.4
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• pp.65-74
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• 2006

The back propagation of neural networks has the problems of falling into local minimum and delay of the speed by the iterative learning. An algorithm to solve the problem and improve the speed of the learning was already proposed in[8], which updates the learning parameter related with the connection weight. In this paper, we propose the algorithm generating initial weight to improve the efficiency of the algorithm by offering the difference between the input vector and the target signal to the generating function of initial weight. The algorithm proposed here can classify more than 98.75% of the handwritten digits and this rate shows 30% more effective than the other previous methods.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.174-183
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• 2017

In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (${\theta}$), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages-Bangla and English. In general, we observed that our proposed contour features outperform other schemes.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.642-649
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• 2011

Among the various aspects of offline handwritten character patterns, it is the great variety of writing styles and variations that renders the task of computer recognition very hard. The immense variety of character shape has been recognized but rarely studied during the past decades of numerous research efforts. This paper tries to address the problem of measuring image distortions and handwritten character patterns with respect to reference patterns. This work is based on mass-spring mesh model with the introduction of simulated electric charge as a source of the external force that can aid decoding the shape distortion. Given an input image and a reference image, the charge is defined, and then the relaxation procedure goes to find the optimum configuration of shape or patterns of least potential. The relaxation process is based on the fourth order Runge-Kutta algorithm, well-known for numerical integration. The proposed method of modeling is rigorous mathematically and leads to interesting results. Additional feature of the method is the global affine transformation that helps analyzing distortion and finding a good match by removing a large scale linear disparity between two images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.705-708
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• 1998

In this paper, a new algorithm is proposed that the template matching is used to devide SOFM (self-organizig feature map) for fast learning and to extract features for considering input data types. In order to verify the superoprity of the proposed algorithm, applied to the recognition of handwritten numerals. Templates of handwritten numerals are created by a line of external-contact.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.468-471
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• 1999

In this paper, we present a solution for combining multiple neural networks. Each neural network is trained with different features. And the neural networks are combined by four methods. The recognition rates by four combination methods are compared. The experimental results for handwritten digit recognition shows that the combination at hidden layers by single layer neural network is superior to any other methods. The reasons of the results are explained.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.904-911
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• 2003

In this paper, we propose an effective method for the distinction between machine-printed and handwritten Korean address images. It is important to know whether an input image is handwritten or machine-printed, because methods for handwritten image are quite different from those of machine-printed image in such applications as address reading, form processing, FAX routing, and so on. Our method consists of three blocks: valid connected components grouping, feature extraction, and classification. Features related to width and position of groups of valid connected components are used for the classification based on a neural network. The experiment done with live Korean address images has demonstrated the superiority of the proposed method. The correct classification rate for 3,147 testing images was about 98.85%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.63-68
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• 2010

Recently, to accelerate the Ubiquitous generation, the input interface of the mobile machinery and tools are actively being researched. In addition with the existing interfaces such as the keyboard and curser (mouse), other subdivisions including the handwriting, voice, vision, and touch are under research for new interfaces. Especially in the case of small-sized mobile machinery and tools, there is a increasing need for an efficient input interface despite the small screens. This is because, additional installment of other devices are strictly limited due to its size. Previous studies on handwriting recognition have generally been based on either two-dimensional images or algorithms which identify handwritten data inserted through vectors. Futhermore, previous studies have only focused on how to enhance the accuracy of the handwriting recognition algorithms. However, a problem arisen is that when an actual handwriting is inserted, the user must select the classification of their characters (e.g Upper or lower case English, Hangul - Korean alphabet, numbers). To solve the given problem, the current study presents a system which distinguishes different languages by analyzing the form/shape of inserted handwritten characters. The proposed technique has treated the handwritten data as sets of vector units. By analyzing the correlation and directivity of each vector units, a more efficient language distinguishing system has been made possible.