A standard iterative thinning algorithm which has been widely used to extract features for character recognition may destroy information due to several defects such as spurious loops in the skeleton and deformation in touched strokes. This is because most thinning algorithms rely on the steady erosion of character boundaries while maintaining the connectivity of the shape. To solve this problem, this paper proposes a knowledge-based thinning method which removes the spurious loops by a preprocessing stage and makes use of average stroke widths and domain knowledge on Hangul (Korean script) to extract intuitive strokes. The experimental results on 2000 handwritten Hangul characters in PE92 benchmark database indicate that the proposed method has reduced the number of defects and led to more intuitive strokes.
|Number of pages||9|
|Journal||Engineering Applications of Artificial Intelligence|
|Publication status||Published - 2003 Feb|
Bibliographical noteFunding Information:
This work was supported in part by Biometrics Engineering Research Center, KOSEF, in Korea.
Dong-Hyeop Han received his B.S. and M.S. degrees from Yonsei University, Seoul, Korea, in 1996 and 1998, respectively. From 1996 to 1997, he joined the project supported by the consortium of offline handwritten character recognition in Korea. Since 1998 he has been appointed as a researcher in Samsung Data Communication Company. His fields of interest include pattern recognition, artificial intelligence, knowledge-based thinning and data mining.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering