Modeling documents for structure recognition using generalized N-grams

We present and discuss a novel approach to modeling logical structures of documents, based on a statistical representation of patterns in a document class. An efficient and error tolerant recognition heuristics adapted to the model is proposed. The statistical approach permits easily automated and incremental learning of the model. The approach has been partially evaluated on a prototype. A discussion of the results achieved by the prototype is finally made