• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.181-189
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• 2002

In this paper, we designed and implemented Avata construction system using XML(extensible Markup Language) and SVG(Scalable Vector Graphic). The Web character created with Avata(or Web character) construction system are displayed in same (on without damage of image, regardless terminal type and user can modify and change image easily in form that want. Compare with existing Web character system, the Reusability of web character part element Is increased greatly with Avata construction system of this paper. Because SVG is described by text, graphic retrieval is convenient, and applications can use easily SVG document. Also, SVG can create web graphic document dynamically with database because can access easily in all graphic primitives of line, Polygon, text, image etc. As well as web character using study finding, we may develop usable technology to some contents on World Wide Web.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.97-100
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• 2013

In this paper, we propose a detection method of the character rather than cursive, containing many components of the vertical and horizontal direction in complex background image. The characters have many dense corners but the background has few sparse corners. So we use harris corner detector and cluster the corners by using the position of the detected corners for detecting character regions. To merge or filter character regions, we analysis a histogram of gray image of character regions. In each improved region, we compare histograms of R, G, B channels to detect characters.

• Journal of the Semiconductor & Display Technology
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• v.11 no.2
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• pp.27-31
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• 2012

This paper proposes a new method of character segmentation in a grayscale image using the standard deviation. Firstly, the proposed method scans vertically the region of interest in an image in order to calculate a standard deviation for each scan line. Characters' standard deviations are much bigger than the background's. Therefore, it is possible to segment characters vertically using the differentiation of those two types of standard deviations. Secondly, the method scans each vertically segmented image horizontally at this time, and then segments each image similarly. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using credit card images. The results show that the proposed algorithm is quite successful for most credit cards. However, the method fails in some credit cards with strong background patterns.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.1-13
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• 2021

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.87-97
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• 2005

This study deals with the analysis on the consciousness and image character of the internet shopping mall. As consciousness analysis result of internet shopping mall consumer, 'cheap price' and 'convenience' are evaluated high by reason that buy goods/service. 'Delivery delay' shows that deficiency of swiftness is indicated preferentially by shortcoming when the goods/service are purchased at the internet shopping mall. Consumer is prferring most 'deferred payment' with consumer's protection system of internet shopping mall. In image character of internet shopping mall, computer system speed and swiftness of reaction time, intimacy of shopping mall site design, delivery system trustability, goods/service contiguity, trustability of billing system, recognition shopping mall company, consistency about good service etc., showed high assessment, but comparative satisfaction is not high in solution at authoritativeness of personal information leakage prevention, problem occurrence.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.66-72
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• 2016

With the growing shift from traditional educational approaches and studying to the more digital classroom, using electronic textbooks and digital native's demand, there is a growing need to develop new methods for learn Kanji characters for children. The purpose of this study is to help children learn the basic Kanji by using stop motion and block methods, and approaching the basic Kanji character education with a more innovative and interactive smart phone APP. In the development of this smart phone App for children's Kanji character education proposed in this study, 100 basic Kanji characters for children are selected. These 100 characters are required for the stop motion animation production, where each selected Kanji is created as a stop-motion animation utilizing a variety of techniques, such as storytelling, to better engage children. The intelligent App is designed with image recognition technology, so that in the learning process children take a picture for the assembled block using their smart phone, the APP then recognizes whether it is assembled correctly, and then plays an animation corresponding to the assembled Kanji character.

• 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.

• 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 Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.444-450
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• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.