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We present a document analysis system able to assign logical labels and extract 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. The generic document knowledge used is identified explicitly. Our system integrates components based on computer vision, artificial intelligence, and natural language processing techniques. Experimental results for each component and for the entire system are presented. The performance of the system is good, especially when considering the variety of documents in the collection. Document Understanding for a Broad Class of Documents

We present a fully implemented system based on generic document knowledge for detecting the logical structure of documents for which only general layout information is assumed. In particular, we focus on detecting the reading order. Our system integrates components based on computer vision, artificial intelligence, and natural language processing techniques. The prominent feature of our framework is its ability to handle documents from heterogeneous collections. The system has been evaluated on a standard collection of documents to measure the quality of the reading order detection. Experimental results for each component and the system as a whole are presented and discussed in detail. The performance of the system is promising, especially when considering the diversity of the document collection.

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