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Rotation invariant hand-drawn symbol recognition based on a dynamic time warping model

  • Alicia FornésEmail author
  • Josep Lladós
  • Gemma Sánchez
  • Dimosthenis Karatzas
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Abstract

One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.

Keywords

Document analysis Graphics recognition Symbol recognition Handwriting recognition Sequence alignment 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Alicia Fornés
    • 1
    Email author
  • Josep Lladós
    • 1
  • Gemma Sánchez
    • 1
  • Dimosthenis Karatzas
    • 1
  1. 1.Computer Vision Center, Department of Computer ScienceUniversitat Autònoma de BarcelonaBellaterraSpain

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