Earticle

초록

영어

A method based on local contour distribution features is proposed for writer identification in this paper. In preprocessing, contours are abstracted form images by an improved Bernson algorithm. Then the Local Contour Distribution Feature (LCDF) is extracted from the fragments which are parts of the contour in sliding windows. In order to reduce the impact of stroke weight, the fragments which do not directly connect the center point are ignored in the feature abstraction procedure. The edge point distributions of the fragments are counted and normalized into LCDFs. At last, the weighted Manhattan distance is used as similarity measurement. The experiments on our database and ICDAR 2011 writer identification database show that the performance of the proposed method reach or exceed those of existing state-of-art methods.

목차

Abstract
 1. Introduction
 2. Feature Abstraction and Similarity Measurement
  2.1. Contour Detection Preprocessing
  2.2. Fragment Extraction
  2.3. The LCDF Extraction
  2.4. Similarity Measurement
 3. Experiments
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Hong Ding [ School of Computer Science and Technology, Nantong University, Nantong, China ]
• Huiqun Wu [ Medical School, Nantong University, Nantong, China ]
• Xiaofeng Zhang [ School of Computer Science and Technology, Nantong University, Nantong, China ]
• JianPing Chen [ School of Computer Science and Technology, Nantong University, Nantong, China ] Corresponding author.

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605