Multistrategy Learning for Document Recognition

In this paper, a methodology for document classification and understanding is proposed. It is based on a multistrategy approach to learning from examples. By document classification, we mean the process of identification of the particular class to which a document belongs. Document understanding is defined as the process of detecting the logical structure of a document. The multistrategy approach for document classification and understanding has been implemented in a system called PLRS, which embeds two empirical learning systems: RES and INDUBI/H. Given a set of documents whose layout structure has already been detected and such that the membership class has been defined by the user; RES generates the knowledge base of an expert system devoted to the classification of a document. The language used to describe both the layout of the training documents and the learned rules is a first-order language. The learning methodology adopted for the problem of learning classification rules integrates both a parametric and a conceptual learning method. As to the problem of document understanding, INDUBI/H can be used to generate the recognition rules, provided that the user is able to supply examples of the logical structure. RES and INDUBI/H are implemented in C language. PLRS is a module of IBIsys, a software environment for office automation distributed by Olivetti.