A Novel Approach to Recover Writing Order From Single Stroke Offline Handwritten Images
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Recovery of a Drawing Order from Off-Line Isolated Letters Dedicated to On-Line Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
Interpretation of Ambiguous Zone in Handwritten Chinese Character Images Using Bayesian Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Recovering drawing order of single-stroke handwritten images using probabilistic tabu search
Journal of Mobile Multimedia
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This paper proposes an efficient method for recovering dynamic information from offline single-stroke hand drawing images. This method makes use of both the local analysis and global smoothness calculation. At first, a graph model is built from the skeleton. Then, odd degree nodes are resolved in a probability framework to detect the double-traced/terminal segments, and even degree nodes are analyzed by the Node Traversing Rule (NTR). We estimate the probability of two strokes being contiguous pair by PCA based angle calculation. Then, double-traced lines are identified. Finally, we calculate the smoothness for each of the possible paths by SLALOM approximation and select the smoothest one. Experiments show that our method works successfully on cursive hand drawing images.