Document image classification is an important step in document image analysis. Based on classification results we can tackle other tasks such as indexation, understanding or navigation in document collections. Using a document representation and an unsupervised classification method, we may group documents that from the user point of view constitute valid clusters. The semantic gap between a domain independent document representation and the user implicit representation can lead to unsatisfactory results.

In this paper we describe document images based on frequent occurring symbols. This document description is created in an unsupervised manner and can be related to the domain knowledge. Using data mining techniques applied to a graph based document representation we find frequent and maximal subgraphs. For each document image, we construct a bag containing the frequent subgraphs found in it. This bag of "symbols" represents the description of a document. We present results obtained on a corpus of 60 graphical document images.We present results obtained on a corpus of 60 graphical document images.