5 research outputs found
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
We present a new approach for recognition of complex graphic symbols in
technical documents. Graphic symbol recognition is a well known challenge in
the field of document image analysis and is at heart of most graphic
recognition systems. Our method uses structural approach for symbol
representation and statistical classifier for symbol recognition. In our system
we represent symbols by their graph based signatures: a graphic symbol is
vectorized and is converted to an attributed relational graph, which is used
for computing a feature vector for the symbol. This signature corresponds to
geometry and topology of the symbol. We learn a Bayesian network to encode
joint probability distribution of symbol signatures and use it in a supervised
learning scenario for graphic symbol recognition. We have evaluated our method
on synthetically deformed and degraded images of pre-segmented 2D architectural
and electronic symbols from GREC databases and have obtained encouraging
recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document
Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10,
1325-132
Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition
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.Comment: 10 pages, Eighth IAPR International Workshop on Graphics RECognition
(GREC), 2009, volume 8, 22-3
Correspondence edit distance to obtain a set of weighted means of graph correspondences.
Given a pair of data structures, such as strings, trees, graphs or sets of points, several correspondences (also referred in literature as labellings, matchings or assignments) can be defined between their local parts. The Hamming distance has been largely used to define the dissimilarity of a pair of correspondences between two data structures. Although it has the advantage of being simple in computation, it does not consider the data structures themselves, which the correspondences relate to. In this paper, we extend the definitions of a recently presented distance between correspondences based on the concept of the edit distance, which we called Correspondence edit distance. Moreover, we present an algorithm to compute the set of weighted means between a pair of graph correspondences. Both the Correspondence edit distance and the computation of the set of weighted means are necessary for the calculation of a more representative prototype between a set of correspondences. In the validation section, we show how the use of the Correspondence edit distance increases the quality of the set of weighted means compared to using the Hamming distance
Symbol Recognition: Current Advances and Perspectives
Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content