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Extraction of Off-Line Handwritten Characters Based on a Soft K-Segments for Principal Curves

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

Abstract

Principal curves are nonlinear generalizations of principal components analysis. They are smooth self-consistent curves that pass through the middle of the distribution. By analysis of existed principal curves, we learn that a soft k-segments algorithm for principal curves exhibits good performance in such situations in which the data sets are concentrated around a highly curved or self-intersecting curves. Extraction of features are critical to improve the recognition rate of off-line handwritten characters. Therefore, we attempt to use the algorithm to extract structural features of off-line handwritten characters. Experiment results show that the algorithm is not only feasible for extraction of structural features of characters, but also exhibits good performance. The proposed method can provide a new approach to the research for extraction of structural features of characters.

Keywords

Off-line handwritten characters features A soft k-segments algorithm for principal curves Structural features Features extraction 

Notes

Acknowledgements

This paper is supported by the National Social Science Fund (Granted No. 13CFX049), Shanghai University Young Teacher Training Program (Granted No. hdzf10008) and the Research Fund for East China University of Political science and Law (Granted No. 11H2K034).

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.Department of Information Science and TechnologyEast China University of Political Science and LawShanghaiPeople’s Republic of China

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