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2015 | OriginalPaper | Buchkapitel

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

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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.

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Metadaten
Titel
Extraction of Off-Line Handwritten Characters Based on a Soft K-Segments for Principal Curves
verfasst von
Na Jiao
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-25783-9_25