Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
An interactive system for recognizing hand drawn UML diagrams
CASCON '00 Proceedings of the 2000 conference of the Centre for Advanced Studies on Collaborative research
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We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.