Automatic annotation extension and classification of documents using a probabilistic graphical model

With the fast growth of document images, document annotation has become a research area of great interest. Annotation allows to describe the semantic content of documents and facilitates their use and research. However, for a huge number of documents, the manual annotation of each document becomes a tedious task. A solution is to annotate a small part of the documents and to extend it automatically to the whole dataset. In this paper, we propose a model for annotation extension and document classification using a probabilistic graphical model. In this latter, we combine visual and textual characteristics and we show that the integration of the user feedback improves the annotation step.