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Finding Significant Points for a Handwritten Classification Task

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Image Analysis and Recognition (ICIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3211))

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Abstract

When objects are represented by curves in a plane, highly useful information is conveyed by significant points. In this paper, we compare the use of different mobile windows to extract dominant points of handwritten characters. The error rate and classification time using an edit distance based nearest neighbour search algorithm are compared for two different cases: string and tree representation.

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© 2004 Springer-Verlag Berlin Heidelberg

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Rico-Juan, J.R., Micó, L. (2004). Finding Significant Points for a Handwritten Classification Task. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_55

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  • DOI: https://doi.org/10.1007/978-3-540-30125-7_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23223-0

  • Online ISBN: 978-3-540-30125-7

  • eBook Packages: Springer Book Archive

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