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
The usage of linguistic information has also been proposed by Aiello et al. REF , who described a document analysis system for logical labelling and reading order extraction of broad classes of documents.
Document Understanding for a Broad Class of Documents
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
Computer Science