Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Holistic handwritten word recognition using temporal features derived from off-line images
Pattern Recognition Letters
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of temporal information of cursively handwritten words for on-line recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Strokes recovering from static handwriting
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
IEEE Transactions on Image Processing
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Pen Trajectories of Multi-Path Static Scripts Using Hidden Markov Models
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Recognition-directed recovering of temporal information from handwriting images
Pattern Recognition Letters
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
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It is generally agreed that an on-line recognitionsystem is always reliable than an off-line one. It is due tothe availability of the dynamic information, especially thewriting sequence of the strokes. This paper presents anew statistical method to reconstruct the writing order ofa handwritten script from a two-dimensional static image.The reconstruction process consists of two phases, namedthe training phase and the testing phase. In the trainingphase, the writing order with other attributes, such aslength and direction, are extracted from a set of trainingon-line handwritten scripts statistically to form auniversal writing model (UWM). In the testing phase,UWM is applied to reconstruct the drawing order of off-linehandwritten scripts by finding the highest totalprobability. 300 off-line signatures with ground truth areused for evaluation. Experimental results show that thereconstructed writing sequence using UWM is close to theactual writing sequence.