Julien Lerouge

Julien Lerouge

Senior Data Scientist @ QuickSign
  • Deep learning
  • Image processing
  • Document analysis & understanding (classification, OCR, NLP)


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

1L3I Laboratory, University of La Rochelle, av M. Crépeau, 17042 La Rochelle Cedex 1, France
2Normandie Université, LITIS EA 4108, University of Rouen, 76801, Saint-Etienne du Rouvray, France

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.