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

Maroua Mehri, Petra Gomez-Kramer, Pierre Heroux, Mickael Coustaty, Julien Lerouge, Remy Mullot
2015 2015 13th International Conference on Document Analysis and Recognition (ICDAR)  
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
more » ... d 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 graphmatching 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 stateof-the-art methods to illustrate the effectiveness of the proposed solutions.
doi:10.1109/icdar.2015.7333757 dblp:conf/icdar/MehriGHCLM15 fatcat:dxjbbtbb3rb3td2u5y3qqjz3la