A Study on the Hand-written Number Recognition by HMM(Hidden Markov Model)

JO, MIN HWAN

KJC Korea
Journal Central

Journal of The Korea Society of Computer and Information 2021 KCI Impact Factor : 0.62

Korean | English

pISSN : 1598-849X / eISSN : 2383-9945

http://journal.kci.go.kr/jksci

BACK
TOP

Home > Explore Content > All Issues > Article View

16 of 29

A Study on the Hand-written Number Recognition by HMM(Hidden Markov Model)

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2004, 9(3), pp.121-125

Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

JO, MIN HWAN ¹

¹춘천교육대학교

ABSTRACT
열림/닫힘 버튼

In the most of recognizing systems of hand-written numbers, extraction of feature shape by using character elements shapes and a method of morphological analysis by using then extraction of feature shapes were usually used. In this paper, however, peculiar chain-code is used, and differential code which gets minimal value by differentiating the chain-code which is generated by the peculiar chain-code is made. We found this differential code is very successful in discriminating hand-written numbers according to the result of applying to most of the hand-written numbers. Testing recognition of hand-written numbers by HMM network. From the results, we can recognize of 96.1 percentage hand-written numbers but can not recognize extremely distorted hand-written numbers.
KEYWORDS
열림/닫힘 버튼

인식(Recognition), 방향성코드(Chain Code), HMM(Hidden Markov Model)

Statistics

120 Viewed

20 Downloaded

Tools

Print this page

Download PDF

Export Citation

Sharing

Facebook

Twitter

Issue List

Vol.27 No.7

Vol.27 No.6

Vol.27 No.5

Citation status

KCI Citation Counts (1)
열림/닫힘 버튼
REFERENCES (9)
열림/닫힘 버튼
* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[journal] Jay June Lee / Data-driven Design of HMM Topology for On-line Handwriting Recognition / KAIST. 2000for Department of Electrical Engineering and Computer Science / Division of Computer Science

[journal] 2111-2115. Sep. 1991 / N. Merhav and Y. Ephraim. Maximun likelihood hidden Markov modeling using a dominant sequence of states. IEEE Trans. on Signal Processing

[journal] J.-Y. Ha / and Jin H. Kim. Unconstrained handwritten word recognition with interconnected hidden Markov models. In Third Int. Workshop on Frontiers in Handwriting Recognition : 455~460

[journal] Michel Gilloux / 1993. / Strategies for Handwritten Words Recognition Using Hidden Markov Models Proc. of 2nd Int. Cong. on Document Analysis and Recognition : 299~304

[journal] H. Bunke / 1995. / Off-Line Cursive Handwriting Recognition Using Hidden Markov Model 28(9) : 1399~1413

[journal] Yannick Tremblay / 1994. / Proc. of 4th Int. Workshop on Frontiers in Handwriting Recognition : 449~455

[journal] Woo Sung Kim / 1996. / Off-line Recognition of Handwritten Korean and Alphanumeric Characters Using Hidden Markov Models 29(5) : 845~858

[journal] 조성목 / 2001. / “이미지 특징 추출연산자 DBAH/DBAG 와 하드웨어 실현” 제6권 (제1호)

[journal] 조민환 / 2003. / “체인코드를 이용한 훼손된 지문의 인식에 관한 연구” ^제8권 (제4호) : 63~68