Collaboration Between Statistical and Structural Approaches for Old Handwritten Characters Recognition

In this article we try to make different kinds of information cooperate in a characters recognition system addressing old Greek and Egyptians documents. We first use a statistical approach based on classical shape descriptors (Zernike, Fourier). Then we use a structural classification method with an attributed graph description of characters and a random graph modeling of classes. The hypothesis, that structural methods bring topological information that statistical methods do not, is validated on Greek characters. A cooperation with a chain of classifiers based on reject management is then proposed. Due to computation cost, the goal of such a chain is to use the structural approach only if the statistical one fails.