Handwritten Word Spotting Based on A Hybrid Optimal Distance

Abstract : In this paper, we develop a comprehensive representation model for handwriting, which contains both morphological and topological information. An adapted Shape Context descriptor built on structural points is employed to describe the contour of the text. Graphs are first constructed by using the structural points as nodes and the skeleton of the strokes as edges. Based on graphs, Topological Node Features (TNFs) of n-neighbourhood are extracted. Bag-of-Words representation model based on the TNFs is employed to depict the topological characteristics of word images. Moreover, a novel approach for word spotting application by using the proposed model is presented. The final distance is a weighted mixture of the SC cost, and the TNF distribution comparison. Linear Discriminant Analysis (LDA) is used to learn the optimal weight for each part of the distance with the consideration of writing styles. The evaluation of the proposed approach shows the significance of combining the properties of the handwriting from different aspects.
Type de document :
Communication dans un congrès
International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. 2014
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https://hal-ujm.archives-ouvertes.fr/ujm-01017645
Contributeur : Christine Largeron <>
Soumis le : mercredi 2 juillet 2014 - 21:25:44
Dernière modification le : jeudi 1 novembre 2018 - 01:19:54

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  • HAL Id : ujm-01017645, version 1

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Christine Largeron, Peng Wang, Véronique Eglin, Christophe Garcia. Handwritten Word Spotting Based on A Hybrid Optimal Distance. International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. 2014. 〈ujm-01017645〉

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