We use collineations as projective information to match and verify the planarity of the 3D features. When extending the matching to the whole sequence of images, some coherence problems can occur and have to be solved before reconstructing the scene. We are designing a new representation of document models that enables the automatic initial learning of a model for a few given examples as well as incremental learning to enhance the model. We are also developing an error tolerant recognition algorithm. Finally ways are explored how the model can be integrated to existing standards used for document production.
Software integration becomes a research topic in itself.
We are studying an original system architecture that drops the strict processing chain, to offer an adequate level of human-machine cooperation. Currently, our investigations concern newspapers that can be downloaded from the web in PDF format. The tools to be developed are intended to be used for an intelligent indexation by the means of structural descriptors containing author names, titles, abstracts, textual content and images. This project deals with font recognition, an often neglected subject in the field of document recognition. The adopted statistical approach consists of identifying the dominant font in a text image block, without any knowledge of the nature of characters appearing in that text.