This paper presents a new method to address the problem of handwritten text segmentation into text lines
and words. Thus, we propose a method based on the cooperation among points of view that enables the
localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of
resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment
the connected components during the analysis. Then, we propose a segmentation of lines into words based on the
cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a
Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce
structural rules in order to take into account some generic knowledge about the organization of a text page.
This cooperation among information gives a bigger power of expression and ensures the global coherence of the
recognition. We validate this work using the metrics and the database proposed for the segmentation contest of
ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods
of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied
kinds of writings, which are the main difficulties met by the other methods.