Interactive content-based Document Retrieval using fuzzy attributed relational graph matching

The evolution of digital technology and the desire to maintain an easy access to scanned documents implies the interest of Document Image Retrieval (DIR). In this paper, we propose a fuzzy graph-based document retrieval approach for determining similarity between document images for content-based document retrieval. To model the structure of document images, we have opted to use Attributed Relational Graphs (ARGs). For each document region (text, graphic, etc) we associate a node which is characterized by a set of fuzzy membership degrees reflecting low-level properties (texture, shape, color, etc). We use this fuzzy description in order to guarantee more robustness against the eventual segmentation errors which may be occurred after the segmentation of document regions. ARGs edges represent spatial relationships between regions which have common boundaries. Finally, we have developed a tree-search based optimal matching algorithm, which allows the search for document according to its structure. The database used for experiments is composed of segmented Coran document images from the National Library of Tunisia. Different weights are assigned for regions and edges according to their relative importance. The results obtained demonstrate the effectiveness of fuzzy graph-based document retrieval. Such an approach is very useful for several applications in many fields.