We address the problems of structuring and annotation of layout-oriented documents.We model the annotation problems as the collective classification on graph-like structures with typed instances and links that capture the domain-specific knowledge. We use the relational de-pendency networks (RDNs) for the collective inference on the multi-typed graphs. We then describe a variant of RDNs where a stacked approximation replaces the Gibbs sampling in orderto accelerate the inference. We report results of evaluation tests for both the Gibbs sampling and stacking inference on two document structuring examples.