• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.38-41
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• 2014

본 논문에서는 필기 문서 영상을 분석하여 단어 단위로 요소들을 분할하는 방법을 제안한다. 일반적으로 인쇄 문서에 비하여 필기 문서에서는 글자 간 간격이 일정하지 않을 뿐만 아니라 필기자 또는 작성된 언어에 따라 특성이 매우 다르게 나타나기 때문에 단어를 분리하는 것은 어려운 문제로 간주되었고 많은 연구가 진행되었다. 제안하는 방법은 이 문제를 해결하기 위하여 글자 획의 두께를 고려하여 정규화시킨 각 연결 요소간 간격과 간격 안에 존재하는 글자 픽셀의 수로 구성된 2 차원의 특징값을 추출하였다. 이 특징값을 바탕으로, 제안하는 방법은 k-평균 클러스터링을 이용하여 각 텍스트라인을 구성하는 연결 요소간 간격을 단어 사이의 간격과 단어 내부 글자간의 간격으로 분류하였다. ICDAR 2013 Handwriting Segmentation Contest 데이터베이스에 대한 실험 결과 제안하는 방법은 가장 우수한 성능을 나타내었다.

• International Journal of Contents
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• v.13 no.3
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• pp.38-42
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• 2017

Documents of the last few decades typically include more than one kind of language, so linguistic classification of each word is essential, especially in terms of English and Korean in handwritten documents. Traditional methods mostly use conventional features of structural or stroke features, but sometimes they fail to identify many characteristics of words because of complexity introduced by handwriting. Therefore, traditional methods lead to a considerably more-complicated task and naturally lead to possibly poor results. In this study, convolutional neural network (CNN) is used for classification of English and Korean handwritten words in text documents. Experimental results reveal that the proposed method works effectively compared to previous methods.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.394-397
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• 2007

본 논문에서는 문자 인식의 특징 선별 방법으로 중간점 알고리즘을 이용하는 방법을 제안한다. 영상자료의 특징들로부터 중간점을 선별하고 심볼패턴을 이용하여 필기체 문자를 인식한다. 이 방법은 사전에 많은 심볼 패턴을 학습해야 하지만 한글과 영어의 높은 인식률을 보이고 있으며, 특히 복잡한 문자들의 경우 좋은 결과를 낸다. 여기서는 중간점 알고리즘으로 입력된 데이터를 심볼 패턴과 비교하고, 심볼 영역에 의해 최적 판별 기저를 탐색한 후, 그것을 특징으로 선택한다. 또한 사전 기능과 투명도 기능을 구현하여 필기체 인식을 이용한 여러 활용 방안을 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1662-1667
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• 2005

In this paper, analysis, design, control and prototype construction of a wearable wire-driven haptic interface called HapticPen is discussed. This device can be considered as a wire driven parallel mechanism which three wires are attached to a pen-tip. Wire tensions are provided utilizing three DC servo motors which are attached to a solid frame on the user's body. This device is designed as input as well as output device for a wearable PC. User can write letters or figures on a virtual plate in space. Pen-tip trajectory in space is calculated using motor encoders and force feedback resulting from contact between pen and virtual plate is provided for constraining the pen-tip motion onto the virtual plane that can be easily setup by arbitrary non-collinear three points in space. In this paper kinematic model, workspace analysis, application analysis, control and prototype construction of this device are presented. Preliminary experiments on handwriting in space show feasibility of the proposed device in wearable environments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.211-216
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• 2001

Recently, the research for the mobile computing such as PDA, Palm PC and wearable computing related technologies is widely under development, specially for the input device. Among the mobile input methods are speech recognition, handwriting recognition and cording type. However these systems have the problems of the data input appraratus like input speed and recognition rate. This paper presents the Glove-based input device which could solve the system's data input problem. By the experimental results suggest the method of proposional input method that utilize the hand's movement is appropriate for the effective mobile input devices.

• Journal of Platform Technology
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• v.6 no.1
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• pp.3-8
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• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.178-186
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• 2003

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.229-232
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• 2009

이 논문에서는 디지털펜과 필기체 인식 기술을 이용한 업무 프로세스 개선 프레임워크인 UFS(Ubiquitos Forms Solution)를 제안하고, 차량점검 서비스를 기반으로 구현 결과를 보인다. 제안한 UFS 프레임워크의 설계 및 구현은 새로운 서비스 개발에 있어 재사용성, 확장성, 이동성, 사용 편이성에 중점을 두었다. UFS 기반 서비스는 디지털펜과 종이를 활용하여 현장 업무 정보를 취득함과 동시에 디지털 사본을 생성하고, 미리 정의된 영역에 대해 필기체 인식을 수행하고, 인식 결과에 대해 최소한의 확인 과정만으로 업무 시스템에 데이터를 자동 입력할 수 있다. 제안한 UFS 프레임워크는 전통적인 종이문서 기반 업무가 많은 헬스케어.건설.교육.공공 분야의 서비스 구현에 적용될 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.148-150
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• 2016

영상 신호에 대해 인공지능적인 프로세스를 수행하는 방법들 중에 우수한 성능을 나타내면서 주목을 끌고 있는 방법으로 Convolution Neural Network(CNN)이 있다. 이를 구성할 때 전반부는 convolution network로 구현되고, 후반부는 Neural Network(NN)로 구현된다. 이때, 전반부에서 convolution 과정을 수행하기 위해 다양한 필터가 사용되는데, 이 필터들의 초기값에 따라 CNN의 성능이 달라지게 된다. 본 논문에서는 CNN의 성능을 향상시키기 위해 convolution network의 초기값을 설정하는 방법에 대해 제안하며, 이를 컴퓨터 실험을 통해 증명하기 위해 필기체 인식이라는 응용 알고리즘을 구현하였다.