A novel learning-free word spotting approach based on graph representation

Christine Largeron (1) , Véronique Eglin (2) , Christophe Garcia (2) , Peng Wang (1, 2) , Llados J. , Fornes A.

1 LHC - Laboratoire Hubert Curien
2 imagine - Extraction de Caractéristiques et Identification

Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of 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. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.

Domains: Machine Learning [cs.LG]

Christine Largeron, Véronique Eglin, Christophe Garcia, Peng Wang, Llados J., et al.. A novel learning-free word spotting approach based on graph representation. 11th IAPR workshop on Document Analysis System (DAS 2014), Apr 2014, Tours, France. ⟨ujm-01017651⟩