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Skeletonization of Low-Quality Characters Based on Point Cloud Model

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Computational Science and Its Applications - ICCSA 2011 (ICCSA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6785))

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Abstract

Skeletonization of low-quality Characters (LCs) is a very difficult problem. Since only detected contours (DCs) are known, existing methods focus on how to extract skeletons only from well located real contours (RCs), named real contour model (RCM), perform very badly. A new model, named point cloud model (PCM) is proposed to replace RCM in extracting skeletons for LCs. PCM can preserve more information for LCs and can obtain satisfied skeletons for LCs based on principal curves. The experimental results also show that our method proposed in this paper can obtain satisfied skeletons for LCs, especially in preserving topology and being consistent with the human perception even in serious quality reduction.

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© 2011 Springer-Verlag Berlin Heidelberg

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Hou, X.L., Liao, Z.W., Hu, S.X. (2011). Skeletonization of Low-Quality Characters Based on Point Cloud Model. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21898-9_52

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  • DOI: https://doi.org/10.1007/978-3-642-21898-9_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21897-2

  • Online ISBN: 978-3-642-21898-9

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