Application of deformable template matching to symbol recognition in handwritten architectural drawings

We propose using deformable template matching as a new approach for recognising characters and lineal symbols in handwritten line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity of the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. The 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 handwritten architectural drawings and experimental results demonstrate that symbols that are highly distorted from ideal shape can be accurately identified