Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework
Ernest Valveny
author
Enric Marti
author
2000
Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm.
DAG;IAM;
exported from refbase (http://refbase.cvc.uab.es/show.php?record=1656), last updated on Fri, 15 Jul 2011 17:02:28 +0200
text
http://refbase.cvc.uab.es/files/VAM2000.pdf
10.1109/ICPR.2000.906057
IAM @ iam @ VAM2000
Proc. 15th Int Pattern Recognition Conf
2000
conference publication
2
239
242
0-7695-0750-6