Fine-Grained Document Genre Classification Using First Order Random Graphs

We approach the general problem of classifying machine-printed documents into genres. Layout is a critical factor in recognizing fine-grained genres, as document content features are similar. Document genre is determined from the layout structure detected from scanned binary images of the document pages, using no OCR results and minimal a priori knowledge of document logical structures. Our method uses attributed relational graphs (ARGs) to represent the layout structure of document instances, and a first order random graphs (FORGs) to represent document genres. In this paper we develop our FORG-based genre classification method and present a comparative evaluation between our technique and a variety of statistical pattern classifiers. FORGs are capable of modeling common layout structure within a document genre and are shown to significantly outperform traditional pattern classification techniques when fine-grained genre distinctions must be drawn.