Recovering Writing Traces in Off-Line Handwriting Recognition: Using a Global Optimization Technique
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Recovering Dynamic Information from Static Handwritten Images
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Bayesian Networks
Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text
International Journal on Document Analysis and Recognition
Skeletonization of ribbon-like shapes based on regularity andsingularity analyses
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
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Interpretation of ambiguous zone is an essential step to recovering dynamic information from handwritten images, which can be seen as to deduce the original motion intention of the writer at the intersection areas. This study presents a novel method to interpret ambiguous zones by constructing a Bayesian belief network. In the initial phase, a graph is built to model the character and several sample points are extracted from each sub-stroke. In the interpreting phase, each pair of sub-strokes is characterized in terms of the comparison of orientation, width, and curvature. Finally, a Bayesian belief network is established to determine the continuous pairs. A series of experiments are conducted on test samples collected from a standard handwritten Chinese text database, and the results show that the proposed method can interpret ambiguous zones effectively.