Document understanding for a broad class of documents

Abstract

We present a document analysis system able to assign logical labels and extract the reading order in a broad set of documents. All information sources, from geometric features and spatial relations to the textual features and content are employed in the analysis. To deal effectively with these information sources, we define a document representation general and flexible enough to represent complex documents. To handle such a broad document class, it uses generic document knowledge only, which is identified explicitly. The proposed system integrates components based on computer vision, artificial intelligence, and natural language processing techniques. The system is fully implemented and experimental results on heterogeneous collections of documents for each component and for the entire system are presented.