We present in this paper a feature selection and weighting method for medieval handwriting images that relies on
codebooks of shapes of small strokes of characters (graphemes that are issued from the decomposition of manuscripts).
These codebooks are important to simplify the automation of the analysis, the manuscripts transcription and the
recognition of styles or writers. Our approach provides a precise features weighting by genetic algorithms and a highperformance
methodology for the categorization of the shapes of graphemes by using graph coloring into codebooks
which are applied in turn on CBIR (Content Based Image Retrieval) in a mixed handwriting database containing
different pages from different writers, periods of the history and quality. We show how the coupling of these two
mechanisms 'features weighting - graphemes classification' can offer a better separation of the forms to be categorized
by exploiting their grapho-morphological, their density and their significant orientations particularities.
An efficient mail sorting system is mainly based on an accurate optical recognition of the addresses on the envelopes.
However, the localizing of the address block (ABL) should be done before the OCR recognition process. The location
step is very crucial as it has a great impact on the global performance of the system. Currently, a good localizing step
leads to a better recognition rate. The limit of current methods is mainly caused by modular linear architectures used for
ABL: their performances greatly depend on each independent module performance. We are presenting in this paper a
new approach for ABL based on a pyramidal data organization and on a hierarchical graph coloring for classification
process. This new approach presents the advantage to guarantee a good coherence between different modules and
reduces both the computation time and the rejection rate. The proposed method gives a very satisfying rate of 98% of
good locations on a set of 750 envelope images.