Image Document Categorization Using Hidden Tree Markov Models and Structured Representations
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Configuration REcognition Model for Complex Reverse Engineering Methods: 2(CREM)
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Page Classification for Meta-data Extraction from Digital Collections
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logical Labeling of Arabic Newspapers using Artificial Neural Nets
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Enhancing document structure analysis using visual analytics
Proceedings of the 2010 ACM Symposium on Applied Computing
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In this paper we present and discuss a novel approach to modeling logical structures of documents, based on a statistical representation of patterns in a document class. An efficient and error tolerant recognition heuristics adapted to the model is proposed. The statistical approach permits easily automated and incremental learning of the model. The approach has been partially evaluated on a prototype. A discussion of the results achieved by the prototype is finally made.