Document Images Indexing with Relevance Feedback : an Application to Industrial Context

Olivier Augereau (1) , Nicholas Journet (1) , Domenger Jean Philippe (1)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique

This article presents a new method to index document images. This work is done in an industrial context where thousands of document images are daily digitized, these images have to be sorted in different classes like payroll, various bills, information letters. We propose a software method which aims to accelerate this task. Usually, the number of document classes is a priori unknown. In this paper, we propose an automatic estimation of this class number. According to this class number, we use a clustering algorithm in order to group document images. After this step, we propose an assisted classification tool based on content based image retrieval method (CBIR). For each cluster, a reference image is automatically selected then considering a similarity measure, the other images are sorted and shown to the user. By interacting with the process, the user can reject wrong images. The user feedback is automatically taken into account to enhance the similarity measure by selecting features. The first tests show that, on average, databases are indexed 3 times faster with our assisted classification method than with a standard manual classification process.

Mots clés: document image clustering; document retrieval; feature selection; relevance feedback; industrial application

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Traitement du texte et du document

Olivier Augereau, Nicholas Journet, Domenger Jean Philippe. Document Images Indexing with Relevance Feedback : an Application to Industrial Context. ICDAR, Sep 2011, Beijing, China. pp.1190-1194. ⟨hal-00649870⟩