서지주요정보
HMM 기반 온라인 한글인식에서의 구조적, 전역적 지식의 적용 = Utilization of structural and global shape knowledge for HMM based Korean handwriting recognition
서명 / 저자 HMM 기반 온라인 한글인식에서의 구조적, 전역적 지식의 적용 = Utilization of structural and global shape knowledge for HMM based Korean handwriting recognition / 조성정.
저자명 조성정 ; Cho, Sung-Jung
발행사항 [대전 : 한국과학기술원, 1998].
Online Access 원문보기 원문인쇄

소장정보

등록번호

8008938

소장위치/청구기호

학술문화관(문화관) 보존서고

MCS 98045

SMS전송

도서상태

이용가능

대출가능

반납예정일

등록번호

9004679

소장위치/청구기호

서울 학위논문 서가

MCS 98045 c. 2

SMS전송

도서상태

이용가능

대출가능

반납예정일

초록정보

This thesis presents a method of applying structural and global shape knowledge to hidden Markov model (HMM) based Korean handwriting recognition. Although successfully used in the Korean handwriting recognition area, HMM needs structural and global shape knowledge because of two problems: weak discrimination power of statistical method and insufficient information of local features. To apply structural shape knowledge, graphemes are divided into basic strokes of lines and circles by analyzing relationship between HMM self transition and a character input. Then, geometric features such as distance and angle between basic strokes are checked to verify graphemes. Position relation between graphemes is also checked to eliminate unusual segmentation of a character. To apply global shape knowledge, three features such as accumulated angle change, duration information of basic strokes and pen-up movement ratio are used. The accumulated angle change is summation of angle change of pen movement in the grapheme. The duration information of basic strokes is related with the length ratio between basic strokes. The pen-up movement ratio is used for discrimination between graphemes and ligatures. All the knowledge is merged into HMM recognition system in the probabilistic framework. The HMM grapheme model generates grapheme boundary hypothesis which is verified by the structural and global knowledge. In the experiment, we use three kinds of data sets amount to 15,250,~ 3,127, ~ 16,427 characters respectively. The result shows improved average recognition rate from 94.8% to 95.6% with reducing 14.5% error.

서지기타정보

서지기타정보
청구기호 {MCS 98045
형태사항 44 p. : 삽도 ; 26 cm
언어 한국어
일반주기 부록 : A, 한글자소 기본획과 HMM상태의 대응
저자명의 영문표기 : Sung-Jung Cho
지도교수의 한글표기 : 김진형
지도교수의 영문표기 : Jin-Hyung Kim
학위논문 학위논문(석사) - 한국과학기술원 : 전산학과,
서지주기 참고문헌 : p. 39-41
주제 은닉마르코프 모델
HMM
한글 인식
필기 인식
온라인
구조적 지식
전역적 지식
기본획
Hidden Markov model
HMM
Korean hadwriting Recognition
On-line
Structural knowledge
Global knowledge
Basic stroke
Primitive stroke
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