Neural computing: theory and practice
Neural computing: theory and practice
Modeling Documents for Structure Recognition Using Generalized N-Grams
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Configuration REcognition Model for Complex Reverse Engineering Methods: 2(CREM)
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
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
Structured Document Labeling and Rule Extraction Using a New Recurrent Fuzzy-Neural System
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Knowledge-based derivation of document logical structure
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Logical Labeling Using Bayesien Networks
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Arabic Newspaper Page Segmentation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
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Logical structure analysis is an important phase in the process of document image understanding. In this paper we propose a learning-based method to label logical components on Arabic newspaper documents. The labeling is driven by artificial neural nets. Each one is specialized in a document class. The first prototype of LUNET has been tested on a set of Arabic newspapers of three document classes. Some promising experimental results are reported.