Document image matching using probabilistic graphical models

A document image matching approach making use of probabilistic graphical models is proposed. The document image is first represented by a tree with the nodes in the tree corresponding to the regions in the image and the edges indicating the parent-child relationships between them, transforming the problem to tree matching. A graphical model, i.e. pairwise Markov Random Field is defined on the tree, in which sense the nodes are considered as random variables and the edges encode the relations among these variables in the probability domain. The tree matching problem is then formulated as Maximum a Posterior (MAP) inference over the graphical model and solved by belief propagation. Since the underlying graphical model is tree-structured, the exact inference can be obtained. With properly defined potential functions in the joint probability represented by the graphical model, the disparity in tree representations caused by different image capturing conditions can be tolerated as demonstrated in the encouraging experimental results.