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Proceedings Paper

A compact representation of character skeleton using skeletal line based shape descriptor
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Paper Abstract

Skeletonization is a quite significant technology for the shape representation in the field of image processing and pattern recognition. In order to explore its application onto the Chinese calligraphy character representation and reconstruction, a skeletal line based shape descriptor has been presented by the authors recently. Its performances evaluated by measurement of skeleton deviation (MSD), number of distorted forks (NDF), number of spurious strokes (NSS) as well as measurement of reconstructability (MR) showed that the skeleton-biased phenomenon can be greatly reduced and the pattern reconstructability near to 100% can be achieved. However, due to the use of dense skeletal line (SL) placement scheme, a lot of memory space is needed for storing the extended and dense SL information; and the computation cost is also rather expensive. Therefore, a compact strategy is presented in this paper to overcome these issues. Instead of storing all the SL information, only the sampled SL with a certain interval will be stored in the skeleton table. By performing the curve-fitting strategy derived from Vandermonde matrix onto the sampled SL information in the skeleton table, both the required skeleton and pattern contour can be readily restored, and the original pattern can thus be reconstructed. The sampling interval (SI) from 1 to 6 are used in our experiments (with 15 Chinese calligraphy characters) and the original method is regarded as the ground truth. Our experimental results show that the memory space can be approximately reduced from 54% (SI = 1) to 92% (SI = 6). The pattern reconstructability can still be maintained from 95% (SI = 1) to 92% (SI = 6). Moreover, the mean execution time of pattern reconstruction can be greatly reduced from 7.814 sec (the original method) to 0.078 sec (the improved method). The results confirm the feasibility of the proposed approach.

Paper Details

Date Published: 6 September 2019
PDF: 16 pages
Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111372P (6 September 2019); doi: 10.1117/12.2529933
Show Author Affiliations
Ming-Te Chao, Yuan Ze Univ. (Taiwan)
Yung-Sheng Chen, Yuan Ze Univ. (Taiwan)

Published in SPIE Proceedings Vol. 11137:
Applications of Digital Image Processing XLII
Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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