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
Making Documents Work: Challenges for Document Understanding
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Artificial Neural Networks for Document Analysis and Recognition
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
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Relational learning: statistical approach versus logical approach in document image understanding
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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This paper proposes an approach to the problem of logical labeling in structured documents, employing a new recurrent neuro-fuzzy system called RFasArt (Recurrent Fuzzy Adaptive System ART based). RFasArt preserves fine characteristics, such as modularity, stability and flexibility of its predecessors Fuzzy ARTMAP and FasArt.In this paper the documents are considered as pseudo-temporal sequences and context information is exploited in an integrated form. Two working prototypes for a MIME-based mailing system and for a digital library were tested with over 90 \% of recognition rates and less ambiguous decisions than in previous systems. A manageable knowledge base was constructed using fuzzy rules easily interpretable by human users, and examples of rule creation and fusion are shown.