  Motivation of our work is to present a new methodology for symbol
recognition. We support structural methods for representing visual associations
in graphic documents. The proposed method employs a structural approach for
symbol representation and a statistical classifier for recognition. We
vectorize a graphic symbol, encode its topological and geometrical information
by an ARG and compute a signature from this structural graph. To address the
sensitivity of structural representations to deformations and degradations, we
use data adapted fuzzy intervals while computing structural signature. The
joint probability distribution of signatures is encoded by a Bayesian network.
This network in fact serves as a mechanism for pruning irrelevant features and
choosing a subset of interesting features from structural signatures, for
underlying symbol set. Finally we deploy the Bayesian network in supervised
learning scenario for recognizing query symbols. We have evaluated the
robustness of our method against noise, on synthetically deformed and degraded
images of pre-segmented 2D architectural and electronic symbols from GREC
databases and have obtained encouraging recognition rates. A second set of
experimentation was carried out for evaluating the performance of our method
against context noise i.e. symbols cropped from complete documents. The results
support the use of our signature by a symbol spotting system.
