A Bayesian classifier for symbol recognition

On symbol and character recognition, many works can be found in the literature [Mori 1992, Chhabra 1998], some of them processing classical problems (structured documents) while

We illustrate the steps of this path by examples from recent and continuing studies that are part of the health research and service support pro- grammes

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

Abstract: This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents..

Keywords Symbol recognition · Descriptor combination · Variable selection · Probabilistic graphical models · Bayesian networks..

We first give a very brief introduc- tion to Inductive Logic Programming and how it can con- tribute to learning visual classes for symbol recognition in section 2.. We then show

It remains to be proven that a symbol recognition method based on graph matching can successfully scale to a large number of model symbols.. Also, it is seldom feasible to

There are 4 differ- ent nodes: the decision node (Pronoun) and three at- tribute nodes which respectively represent the fact that the occurrence occurs at the beginning of the