문서 양식 식별을 이용한 광학 문자 인식 시스템
- 주제(키워드) 광학문자인식 , 문서양식식별 , 문서전자화 , OCR
- 발행기관 고려대학교 정보경영공학전문대학원
- 지도교수 장동식
- 발행년도 2009
- 제출일 2009-01-06
- 학위수여년월 2009. 2
- 학위명 석사
- 학과 정보경영공학전문대학원 정보경영공학과
- 세부전공 경영공학
- 원문페이지 45 p
- 실제URI http://www.dcollection.net/handler/korea/000000007679
- 본문언어 한국어
- 제출원본 000045533002
초록/요약
As the importance of keeping documents and papers rises, more companies and organizations tends to use the document digitalizing system in order to manage the documents and papers for their conveniences. For this document digitalization, in past, people had to input all of the data of the paper from top to bottom manually. Now, to reduce these inconvenience, people scan the documents, and then use OCR, Optical Character Recognition, from the scanned image for the document digitalization. However, even after recognizing process using OCR, there are still problems that people have to classify the necessary part and the unnecessary part manually which is again inconvenient. In order to solve above problem, this paper propose a method and a system that recognizing characters after identifying a specific part for character recognition in the document form. The proposed system will be able to improve the efficiency of character recognition by the increase of its speed and its accurateness. Moreover, I expect it is also an effective proposal for a large amount of fixed formed documents.
more목차
제 1 장 서론 ··································································· 1
1.1 연구의 배경 및 목적 ············································· 1
1.2 논문의 구성 ··························································2
제 2 장 관련연구 ·····························································3
2.1 문자 인식 방법 ······················································ 3
2.2 패턴 인식 알고리즘 ··············································· 5
2.2.1 BP 알고리즘 ·············································· 5
2.2.2 K-Means 알고리즘 ···································· 7
2.2.3 SOM 알고리즘 ··········································· 8
제 3 장 시스템 구성 ···················································· 11
3.1 문자 학습부 ··························································12
3.2 문서 양식 설정부 ··················································12
3.3 이미지 등록부 ·······················································13
3.4 문자 인식부 ··························································13
3.5 데이터 저장부 ······················································14
제 4 장 시스템 구현 방법 ············································· 15
4.1 문자 학습 단계 ·····················································15
4.2 문서 양식 설정 단계 ·············································20
4.3 문자 인식 단계 ····················································20
제 5 장 실험 결과 ························································· 24
5.1 구현 화면 ·····························································24
5.2 실험 ·····································································27
5.3 성능 평가 ·····························································28
제 6 장 결론 및 향후 과제 ············································· 31
6.1 결론 ·····································································31
6.2 향후 과제 ·····························································32
참고 문헌 ·······································································33
