Logical Labeling of Document Images Using Layout Graph Matching with Adaptive Learning
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Structure analysis and generation for internet documents
Intelligent exploration of the web
Logical Structure Analysis and Generation for Structured Documents: A Syntactic Approach
IEEE Transactions on Knowledge and Data Engineering
Document Understanding System Using Stochastic Context-Free Grammars
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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Extracting structural information from paper documents supports the daily document processing by, for example, automatically finding index terms, document topics, etc. Knowledge about such components are modeled in a semantic net, which describes geometric properties, spatial relationships, lexical entities as well as lexical relationships. The document model is used to extract the sender, date, recipient, opening and closing formula from a business letter. 181 business letters have been processed, divided into a training set of 20 and the remaining ones for testing. The error rates for the test set range from 0.022 to 0.049 by an average rejection rate of 0.4. Results show that the computational effort can be limited to O(n/sup 2/) given n primitive objects for matching.