Handwritten Text Recognition Using a Multiple-agent Architecture to Adapt the Recognition Task

This communication investigates the automatic reading of unconstrained omni-writer handwritten texts. It shows how to endow the reading system with learning faculties necessary to adapt the recognition to each writer’s handwriting. In the first part of this communication, we explain how the recognition system can be adapted to a current handwriting by exploiting the graphical context defined by the writer’s invariants. This adaptation is guaranteed by activating interaction links over the whole text between the recognition procedures of word entities and those of letter entities. In the second part, we justify the need of an open multipleagent architecture to support the implementation of such a principle of adaptation. The proposed platform allows to plug expert treatments dedicated to handwriting analysis. We show that this platform helps to implement specific collaboration or cooperation schemes between agents which bring out new trends in the automatic reading of handwritten texts.