• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2423-2428
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• 2015

We have implemented a personal prescription management system which enables resource-limited mobile device to utilize the optical character recognition technique. The system enables us to automatically detect and recognize the text in the personal prescription by using a optical character recognition technique. We improved the recognition rate over a pre-processing in order to improve the character recognition rate of the original method. The examples such as a personal prescription management service, alarm service, and drug information service with mobile devices have been demonstrated by using the our system.

• Journal of Korea Multimedia Society
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• v.2 no.4
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• pp.390-399
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• 1999

Extracting the character region(container identifier) in the image of a shipping container is one of the key factors in a system for identifying a shipping container automatically To improve the performance of the automatic recognition system for identifying a shipping container, thus a method partitioning the character region more correctly and efficiently is needed. In this paper, an efficient method is proposed to extract only the character region in the image of a shipping container. The proposed method removes noises that are not possibly related to the character using morphological operation, then the image is binarized using the threshold value that is determined from the image obtained previous step. Finally individual character area is extracted from the binary image. Also experiments are conducted to verify the efficiency of the proposed method. The results show that the proposed method partitions the character region correctly from container images.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.389-391
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• 2007

This paper describes about the printed character segmentation and recognition system for slabs in steel manufacturing process. To increase the recognition rate, it is important to improve success rate of character segmentation. Since Slabs front area surface are not uniform and surface temperature is very high, marked characters not only undergo damages but also have much noise. On the other hand, since almost marked characters are very thick and the space between characters is only about 10 $^{\sim}$ 15 mm, there are many touching characters. Therefore appropriate character image preprocessing and segmentation algorithm is needed. In this paper we propose a multi-local thresholding method for damaged character restoration, a modified touching character segmentation, algorithm for marked characters. Finally a effective Multi-Class SVM is used to recognize segmented characters.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.149-156
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• 2021

Today, 3D softwares have become an essential tool in all areas of Video, including Movies, Animations, CFs, Motion Graphics and Games. One of the most commonly used fields is the 3D character video part. However, these 3D character animations and motion graphics softwares are difficult to learn and too much to learn, making it difficult to learn them all in a university education with a limited time of four years. In this paper, many Inverse kinematics tools, which are essential in the 3D character animations and motion graphics field, compare and analyze the strengths and weaknesses of each tool, focusing on Bone, Character Studio, and Character Animation Toolkit, which are most commonly used in work fields. And use Delphi techniques for 3D experts to secure objectivity. Therefore, for universities that require large amounts of teaching in a limited time, I propose an analysis of which of the above three Inverse Kinetics tools is advantageous for students to select and focus on for efficient education.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.518-523
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• 2001

This paper proposes the new method which is adopted in extracting character features and recognizing characters using fractal dimension of the Chaos theory which highly recolonizes a minute difference with strange attractor created from Henon system. This paper implements a high precision character recognition system. firstly, it gets features of mesh, projection and cross distance feature from character images. And their feature is converted into data of time series. Then using modified Henon system suggested in this paper, each characters attractor about standard Korean Character, KSC 5601 is reconstructed. Secondly, in order to analyze the Chaotic degree of each characters attractor, it gets last features of character image after calculating box-counting Dimension, Natural Measure, Information Bit, Information Dimension which are meant fractal dimension. An experimental result shows 97.49% character classification rates for 2350 Korean characters using proposed method in this paper.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.1-6
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• 1992

This paper proposed a neural computer architecture for the learning of script character pattern recognition categories. Oriented filter with complex cells preprocess about the input script character, abstracts contour from the character. This contour normalized and inputed to the ART. Top-down attentional and matching mechanisms are critical in self-stabilizing of the code learning process. The architecture embodies a parallel search scheme that updates itself adaptively as the learning process unfolds. After learning ART self-stabilizes, recognition time does not grow as a function of code complexity. Vigilance level shows the similarity between learned patterns and new input patterns. This character recognition system is designed to adaptable. The simulation of this system showed satisfied result in the recognition of the hand written characters.

• Journal of the Korean Institute of Telematics and Electronics
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• v.11 no.1
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• pp.23-33
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• 1974

This study has developed a new system of character generator which is able to display more thats 14,364 different Korean characters by the 24 fundamental input elements. That is. in this system, all of the Korean characters are fomalized into 30 kinds of character forms (each having 70-980 characters), from which six form featuares are detacted to control and combine the fundamental input elements at their proper sizes and positions in a character frame. The device is very simple and all of the Korean characters can be displayed on CRT by the operation of only 24 input keys in good character quality.

• The Journal of Engineering Research
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• v.1 no.1
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• pp.59-64
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• 1997

More than 2,000 different chinese characters are used daily in Korea newspapers and publications. The large repertoire of character pattern are the main difficulties when machine recognition of chinese characters is concerned. The goal of this paper is to conceive, evaluate and refine techniques for high performance Chinese character recognition. A new character classifier was being developed using prototype creation method.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1649-1657
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• 2008

In this paper, to overcome the failure of binarization for the characters suffered from low contrast and non-uniform illumination in license plate character recognition system, we improved the binarization method by combining local thresholding with global thresholding and edge detection. Firstly, apply the local thresholding method to locate the characters in the license plate image and then get the threshold value for the character based on edge detector. This method solves the problem of local low contrast and non-uniform illumination. Finally, back-propagation Neural Network is selected as a powerful tool to perform the recognition process. The results of the experiments i1lustrate that the proposed binarization method works well and the selected classifier saves the processing time. Besides, the character recognition system performed better recognition accuracy 95.7%, and the recognition speed is controlled within 0.3 seconds.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.