Interpreting word recognition decisions with a document database graph

A method is presented to filter the output of a word recognition algorithm, which may contain errors, to locate decisions that should be correct with a high degree of certainty. The algorithm uses the output of a word recognition system and techniques used in information retrieval to characterize a free-text document database to locate a set of documents that have topics which are similar to that of the input document. The vocabulary from these similar documents is then used to locate the correct word recognition decisions. Experimental results show that a subset of the word recognition decisions for an input document can be located that are between 90 and 99 percent correct. The subset located by this method can be used to drive other recognition processes applied to the rest of the text.