Efficient estimation of pen trajectory from off-line handwritten words

This paper presents an easy and efficient method to estimate the pen trajectory based on minimizing the pen movement. Given start and end vertices, the complexity of the proposed algorithm is linear. In addition, the algorithm clearly identifies alternatives that do not affect the overall length of the pen trajectory, making enough room for other criteria, e.g. vision rules, to be applied.

[1]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[3]  Patrick Bouthemy,et al.  Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tong Huang,et al.  A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[5]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[6]  S. Jager.  Recovering writing traces in off-line handwriting recognition: using a global optimization technique , 1996 .

[7]  Patrick Bouthemy,et al.  A region-level motion-based graph representation and labeling for tracking a spatial image partition , 2000, Pattern Recognit..

[8]  Murray J. J. Holt,et al.  Processing of Off-Line Handwritten Text: Polygonal Approximation and Enforcement of Temporal Information , 1994, CVGIP Graph. Model. Image Process..

[9]  Horst Bunke,et al.  Recovery of temporal information of cursively handwritten words for on-line recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[10]  Michael J. Black,et al.  Estimating Optical Flow in Segmented Images Using Variable-Order Parametric Models With Local Deformations , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Michal Irani,et al.  Detecting and Tracking Multiple Moving Objects Using Temporal Integration , 1992, ECCV.

[12]  Angelo Chianese,et al.  Recovering dynamic information from static handwriting , 1993, Pattern Recognit..

[13]  Ioannis Patras,et al.  Video Segmentation by MAP Labeling of Watershed Segments , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Makoto Yasuhara,et al.  Recovery of Drawing Order from Single-Stroke Handwriting Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..