Recovery of Drawing Order from Single-Stroke Handwriting Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rule-based cleanup of on-line English ink notes
Pattern Recognition
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
Techniques for static handwriting trajectory recovery: a survey
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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Non-constrained handwriting recognition is still faced with very difficult problems. However, on-line recognition methods exhibit better results than off-line methods which lose all temporal information. The aim of the work presented here is to recover the strokes ordering from static 2-D images as it is inherently available from on-line systems. The approach presented here is innovative, first conversely to most other ones, it uses extensively the gray-level information, secondly because of the use of the Kalman filtering approach in the prediction of the writing strokes trajectory.