A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance

Christine Largeron 1 Véronique Eglin 2 Christophe Garcia 2 Peng Wang 1, 2
1 LHC - Laboratoire Hubert Curien [Saint Etienne]
2 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-tofine handwritten word spotting approach based on the graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
Keywords : word spotting coarse-to-fine mechamism graphbased representation graph embedding graph edit distance
Type de document :
Communication dans un congrès
International Conference on Pattern Recognition (ICPR 2014), Aug 2014, Stockholm, Sweden. 2014
Liste complète des métadonnées
https://hal-ujm.archives-ouvertes.fr/ujm-01017646
Contributeur : Christine Largeron <>
Soumis le : mercredi 2 juillet 2014 - 21:29:30
Dernière modification le : mercredi 25 juillet 2018 - 14:05:31

Identifiants

  • HAL Id : ujm-01017646, version 1

Collections

UNIV-ST-ETIENNE | EC-LYON | LIRIS | LAHC | UNIV-LYON2 | INSA-LYON | UNIV-LYON1 | IOGS | PARISTECH

Citation

Christine Largeron, Véronique Eglin, Christophe Garcia, Peng Wang. A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance. International Conference on Pattern Recognition (ICPR 2014), Aug 2014, Stockholm, Sweden. 2014. 〈ujm-01017646〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

595

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD