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A bottom-up method using texture features and a graph-based representation for lettrine recognition and classification

Abstract : This article tackles some important issues relating to the analysis of a particular case of complex ancient graphic images, called " lettrines " , " drop caps " or " ornamental letters ". Our contribution focuses on proposing generic solutions for lettrine recognition and classification. Firstly, we propose a bottom-up segmentation method, based on texture, ensuring the separation of the letter from the elements of the background in an ornamental letter. Secondly, a structural representation is proposed for characterizing a lettrine. This structural representation is based on filtering automatically relevant information by extracting representative homogeneous regions from a lettrine to generate a graph-based signature. The proposed signature provides a rich and holistic description of the lettrine style by integrating varying low-level features (e.g. texture). Then, to categorize and classify lettrines with similar style, structure (i.e. ornamental background) and content (i.e. letter), a graph-matching paradigm has been carried out to compare and classify the resulting graph-based signatures. Finally, to demonstrate the robustness of the proposed solutions and provide additional insights into their accuracies, an experimental evaluation has been conducted using a relevant set of lettrine images. In addition, we compare the results achieved with those obtained using the state-of-the-art methods to illustrate the effectiveness of the proposed solutions.
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Submitted on : Wednesday, December 2, 2015 - 9:15:23 PM
Last modification on : Thursday, May 12, 2022 - 3:36:47 PM
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Maroua Mehri, Petra Gomez-Krämer, Pierre Héroux, Mickaël Coustaty, Julien Lerouge, et al.. A bottom-up method using texture features and a graph-based representation for lettrine recognition and classification. International Conference on Document Analysis and Recognition (ICDAR), Aug 2015, Nancy, France. pp.226-230, ⟨10.1109/ICDAR.2015.7333757⟩. ⟨hal-01237207⟩



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