Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition
Ernest Valveny
author
Enric Marti
author
2000
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.
DAG;IAM;
exported from refbase (http://refbase.cvc.uab.es/show.php?record=1655), last updated on Fri, 15 Jul 2011 17:23:13 +0200
text
http://refbase.cvc.uab.es/files/MVA2000.pdf
10.1007/3-540-40953-X_16
IAM @ iam @ MVA2000
Graphics Recognition Recent Advances
Springer Verlag
author
2000
Springer Verlag
continuing
periodical
academic journal
1941
193
208