MULTISTRATEGY LEARNING FOR DOCUMENT RECOGNITION

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Abstract
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 INDUBIIH. 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, INDUBIIH can be used to generate the recognition rules, provided that the user is able to supply examples of the logical structure. RES and INDUBIIH are implemented in C language. PLRS is a module oflBIsys, a software environment for office automation distributed by Olivetti.

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