A new approach to discover interlacing data structures in high-dimensional space
Journal of Intelligent Information Systems
Cascade Markov random fields for stroke extraction of Chinese characters
Information Sciences: an International Journal
Mining interlacing manifolds in high dimensional spaces
Proceedings of the 2011 ACM Symposium on Applied Computing
Recognition of Bangla compound characters using structural decomposition
Pattern Recognition
Hi-index | 0.00 |
This paper presents Markov random fields (MRFs) to segment strokes of Chinese characters. The distortions caused by the thinning process make the thinning-based stroke segmentation difficult to extract continuous strokes and handle the ambiguous intersection regions. The MRFs reflect the local statistical dependencies at neighboring sites of the stroke skeleton, where the likelihood clique potential describes the statistical variations of directional observations at each site, and the smoothness prior clique potential describes the interactions among observations at neighboring sites. Based on the cyclic directional observations by Gabor filters, we formulate the stroke segmentation as an optimal labeling problem by the maximum a posteriori (MAP) criterion. The results of stroke segmentation on the ETL-9B character database are encouraging.