• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.51.5-51
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• 2002

In this paper, we propose and developed a keyglove using the touch-typing method as new solutions to the problem of text input into the mobile computing devices. This device recognizes that character is typed in though the hand's movements analysis and requires no additional space on a person's desktop or work surface, and can be easily used with computers of any size, even the smallest mobile computer, and is designed as an input device for wearable computers and virtual environment. The concept of the wearable input device based on human hand-molies recognition.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1515-1524
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• 2008

This paper proposes a method that detects a license plate in complex background using a multiple-color model and character layout information. A layout of a green license plate is different from that of a white license plate. So, this study used a strategy that firstly assumes the plate color and then utilizes its layout information. At first, it extracts green areas from an input image using a multiple-color model which combined HIS and YIQ color models with RGB color model. If green areas are detected, it searches the character layout of the green plate by analyzing the connected components in each areas. If not detected, it searches the character layout of the white plate in all area. Finally, it extracts a license plate by grouping the connected components which corresponds to characters. Experimental result shows that 98.1% of 419 input images are correctly detected. It also shows that the proposed method is robust against illumination, shadow, and weather condition.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.105-116
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• 2019

Objectives : There are many secluded Chinese characters - so called Byeokja (僻字) in ancient classic literature, and Chinese characters that are not registered in Unicode and Variant characters (heterogeneous characters) that cannot be found in the current font sets often appear. In order to register all possible Chinese characters including such characters as units of information exchange, this study attempts to propose a method to encode the morphological information of Chinese characters according to certain rules. Methods : This study suggests the methods to encode the connection between the nodules constituting the Chinese character and the coordinates of the nodules. In addition to that, rules for expressing information about curves, expressions of aspect ratios of characters, rules for minimizing coordinate lines, and rules for expressing aggregation status of character components are added. Results : Through the proposed method, it is possible to generate codes of a certain length by extracting only information expressing the morphological configuration of characters. Conclusions : The method of character encoding proposed in this study can be used to distinguish variant characters with small variations in Byeokja, new Chinese characters and character strokes and to store and search them.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.215-216
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• 2016

본 논문에서는 물리적 버튼이 존재하지 않는 평면방식의 스마트 기기를 시각 장애인이 기존 음성 인식 기반의 문자 입력 방식 대비 3배 이상 빠른 속도로 문자를 입력할 수 있는 제스처 기반 가상 문자 입력 방법을 제안한다. 제안하는 방식의 원리는 스마트폰의 터치화면을 8개 방향으로 설정하고 사용자의 드래그 동작에 따른 입력값의 초기위치와 중앙점, 드래그 형태 등을 이용하여 숫자, 영문, 한글, 특수기호 등을 입력하거나, 스마트기기의 다양한 응용 프로그램들을 자연스럽게 제어하고 동작시킬 수 있는 제스처 기반의 문자입력 방법이다. 실제 시각장애인의 필드 테스트를 통해 제안하는 방식의 효율성을 입증해보인다.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.61-72
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• 2020

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.885-892
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• 2013

In this paper, we propose a method of effectively segmenting character regions by using texture and depth features in 3D stereoscopic images. The suggested method is mainly composed of four steps. The candidate character region extraction step extracts candidate character regions by using texture features. The character region localization step obtains only the string regions in the candidate character regions. The character/background separation step separates characters from background in the localized character areas. The verification step verifies if the candidate regions are real characters or not. In experimental results, we show that the proposed method can extract character regions from input images more accurately compared to other existing methods.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.376-382
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• 2010

We propose a novel method to precisely detect car license plates by locating main characters, which are printed with large font size. The regions of the main characters are directly detected without detecting the plate region boundaries, so that license regions can be detected more precisely than by other existing methods. To generate a binary image, multiple thresholds are applied, and segmented regions are selected from multiple binarized images by a criterion of size and compactness. We do not employ any character matching methods, so that many candidates for main character groups are detected; thus, we use a neural network to reject non-main character groups from the candidates. The relation of the character regions and the intensity statistics are used as the input to the neural network for classification. The detection performance has been investigated on real images captured under various illumination conditions for 1000 vehicles. 980 plates were correctly detected, and almost all non-detected plates were so stained that their characters could not be isolated for character recognition. In addition, the processing time is fast enough for a commercial automatic license plate recognition system. Therefore, the proposed method can be used for recognition systems with high performance and fast processing.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.1025-1031
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• 2000

Pre-existing pattern recognition techniques, in the case of character recognition, have limited on the application field. But CAI character learning system and writer's recognition system are very important parts. The application field of pre-existing system can be expanded in the content that the learning of characters and the recognition of writers in the proposed paper. In order to achieve these goals, the development contents are the following: Firstly, pre-processing method by understanding the image structure is proposed, secondly, recognition of characters are accomplished b the histogram distribution characteristics. Finally, similarity measure functions are defined from standard character pattern for matching of the input character pattern. Also the effectiveness of this system is demonstrated by experimenting the standard primitive image.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1707-1710
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• 2003

In this paper, we propose a method of decision on blurring for business card images using block classification. In the proposed method, an input image is partitioned into 8${\times}$8 blocks and each block is classified into character block or background block using a block energy calculated in DCT domain. Whether the input image is blurring or non-blurring is determined using a ratio of low frequency energy and high frequency energy in DCT domain. Experimental results show that the proposed block classification classifies block well and the proposed decision on blurring decides well for various business card images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.783-789
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• 1991

A learing method for the recognition of printed Chinese character by using the input driven multi-layer perceptron model was proposed and the circuit representing the learning result was designed. In learning the extracted features from Chinese characters are used as inputs and the synapse's weight is integer value. So it is possible to implement the learning result with CMOS circuit.