On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Recovery of Writing Sequence of Static Images of Handwriting using UWM
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Handwritten character skeletonisation for forensic document analysis
Proceedings of the 2005 ACM symposium on Applied computing
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
Techniques for static handwriting trajectory recovery: a survey
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Performance analysis of a proposed smoothing algorithm for isolated handwritten characters
International Journal of Artificial Intelligence and Soft Computing
Artistic line-drawings retrieval based on the pictorial content
Journal on Computing and Cultural Heritage (JOCCH)
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This paper presents a segmentation method that partly mimics the cognitive-behavioral process used by human subjects to recover motor-temporal information from the image of a handwritten word. The approach does not exploit any thinning or skeletonization procedure, but rather a different type of information is manipulated concerning the curvature function of the word contour. In this way, it is possible to detect the parts of the image where the original odometric information is lost or ambiguous (such as, for example, at an intersection of the handwritten lines) and interpret them to finally recover a part of the original temporal information. The algorithm scans the word, following the natural course of the line, and attempts to reproduce the same movement as executed by the writer during the generation of the word. It segments the cursive trace where the contour shows the slow-down of the original movement (corresponding to the maximum curvature points of the curve). At the end of the scanning process, a temporal sequence of motor strokes is obtained which plausibly composed the original intended movement