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Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition

  • Ernest Valveny
  • Enric Martí
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.

Keywords

Input Image Prior Probability Bayesian Inference Bayesian Framework Ideal Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ernest Valveny
    • 1
  • Enric Martí
    • 1
  1. 1.Computer Vision Center — Computer Science DepartmentUniversitat Autònoma de BarcelonaBellaterraSpain

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