• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1825-1831
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• 1999

In this paper, to reduce misrecognized characters, we propose the new method that extract features from character to apply to the character recognition using features from character to apply to the character recognition using fractal dimensions and attractors. Firstly, to reduce the load of recognizer we classify the characters. For the classified character, we extract the features for Box-counting dimensions. Natural Measures, Information dimensions then recognize characters. With histogram, we generate attractors and calculate dimensions from attractors. Then we recognize characters with dimensions of characters and attractors. An experimental result that the overall recognition rates for the training data and testing data are 96.03% and 91.74% respectively. This result shows the effectiveness of proposed method.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.145-148
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• 2000

This paper proposes a character segmentation method for Korean letter address image. The poor quality of image binarization results in broken character strokes. To overcome this problem, two steps of processing ate introduced. The first one is to merge broken characters to generate character candidates, and the other one is to reduce the complexity of segmentation graph path. These two steps do not use recognition information to keep in high-speed.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.223-228
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• 2016

Nowadays users of Virtual Environment are want to play with thousands of players in an evolving virtual world at the same time over the internet. So, the load of this kind of Virtual Environments is heavier than that of any other precedents. One of load balancing methods is map-partition to divide the load of entire system which is vulnerable to delay message between clients and servers. In this paper, we propose a Migration Agent application server architecture using to help migration of player character between field servers and to reduce response time between clients and field servers. Migration Agent is reduce Player Character's responds time as Cache Server, if Player Character move to another Field Server, Player Character need the synchronization process in the DBMS approach, to minimize response time by reducing the period for cross - Player Character Field Server to perform the role. Field Server by placing them in form of a stack existing form of grid, for load concentrated on a specific server.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.522-532
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• 1995

Multilayer neural networks with backpropagation learning algorithm are widely used for pattern classification problems. For many real applications, it is more important to reduce the misclassification rate than to increase the rate of successful classification. But multilayer perceptrons(MLP's) have drawbacks of slow learning speed and false convergence to local minima. In this paper, we propose a new method for character recognition problems with a single-layer network and double rejection mechanisms, which guarantees a very low misclassification rate. Comparing to the MLP's, it yields fast learning and requires a simple hardware architecture. We also introduce a new coding scheme to reduce the misclassification rate. We have prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns, and have applied the proposed method for printed character recognition, which shows that the method reduces the misclassification rate significantly without sacrificing the correct recognition rate.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.103-110
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• 1990

This paper studies on an on-line recognition of handwritten Hangeul characters using the fuzzy inference. To solve the ambiguity due to the variations of writing style by writes, these handwri-tten characters are recognized by means of the fuzzy inference on the production rule which is generated with every relative position information between strokes. In order to reduce the processing time, a subgroup which is previously classified with the number of strokes of reference characters is selected according to the number of strokes of input character, and the tolerance limit of distances between input character and reference characters of a subgroup is introduced to reduce the reference characters which is applied to the fuzzy inference. Experimental results for handwritten Hanguel charters 3990 by 10 writers show the recognition rate of $99.5{\%}$and the average processing time of 0.4sec/character.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.152-158
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• 2005

By using a combination of different feature sets extracted from input character patterns, we can improve the character recognition system performance. To reduce the dimensionality of the combined feature vector, we conduct the feature selection. This paper proposes a general framework for the feature combination and selection for character recognition problems. It also presents a specific design for the handwritten numeral recognition. Tn the design, DDD and AGD feature sets are extracted from handwritten numeral patterns, and a genetic algorithm is used for the feature selection. Experimental result showed a significant accuracy improvement by about 0.7% for the CENPARMI handwrittennumeral database.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.241-248
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• 2023

GAN (Generative Adversarial Networks) creates highly sophisticated counterfeit products by learning real images or text and inferring commonalities. Therefore, it can be useful in fields that require the creation of large-scale images or graphics. In this paper, we implement GAN-based game character creation AI that can dramatically reduce illustration design work costs by providing expansion and automation of game character image creation. This is very efficient in game development as it allows mass production of various character images at low cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1318-1324
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• 2012

This paper proposes a novel method for Game character classification based on texture and edge orientation feature. The character dose not move(NPC) and move the character is classified. Classification of property within the character of straight line segments are used to extract features. First, the character inside edge feature extraction and then calculates EEDH, SSPD. The extracted attribute represents the energy of a particular direction. Thus, these properties were used to classify of NPC and Monster. The proposed method, the user can reduce the unnecessary time in the game.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.11a
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• pp.237-240
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• 2004

This paper describes a character retrieval system for TV programs and a set of novel algorithms for detecting and recognizing faces for the system. Our character retrieval system consists of two main components: Face Register and Face Recognizer. The Face Register detects faces in video frames and then guides users to register the detected faces of interest into the database. The Face Recognizer displays the appearance interval of each character on the timeline interface and the list of scenes with the names of characters that appear on each scene. These two components also provide a function to modify incorrect results. which is helpful to provide accurate character retrieval services. In the proposed face detection and recognition algorithms. we reduce the computation time without sacrificing the recognition accuracy by using the DCT/LDA method for face feature extraction. We also develop the character retrieval system in the form of plug-in. By plugging in our system to a cataloguing system. the metadata about the characters in a video can be automatically generated. Through this system, we can easily realize sophisticated on-demand video services which provide the search of scenes of a specific TV star.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.