A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases

A sigma-lognormal model-based approach to generating large synthetic online handwriting sample... This article describes a methodology to generate a large database of synthetic samples from a small set of original online handwriting specimens. The overall paradigm is based on the Kinematic Theory of rapid human movements and its sigma-lognormal model. The principal contributions of the present study include (i) development of a strategy for sigma-lognormal model-based generation of synthetic samples from real online handwriting samples of arbitrary scripts captured by arbitrary relevant devices and (ii) verification of the structural similarities, including the naturalness of such synthetic prototypes, through various human perception experiments, computer evaluations and statistical hypothesis testing. A database consisting of a large number of online synthetic handwritten word samples is used to train and evaluate the performance of three existing automatic online handwriting recognition systems. Training based on a combined set of original and synthetic samples improves the recognition accuracies on the test set. A combined training set is useful irrespective of the nature of the feature set used (online, offline or combined). Although the proposed method has primarily been developed and applied to the design of an online handwriting sample database of a popular Indian script, Bangla, it can be applied to the generation of large databases of any arbitrary script for example: English, Chinese and Arabic. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Document Analysis and Recognition (IJDAR) Springer Journals

A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Image Processing and Computer Vision; Pattern Recognition
ISSN
1433-2833
eISSN
1433-2825
D.O.I.
10.1007/s10032-017-0287-5
Publisher site
See Article on Publisher Site

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