Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Vectorial Signatures for Symbol Discrimination

Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we present a method based on vectorial signatures, which aims at discriminating, by a fast technique, symbols represented within technical documents. The use of signatures on this kind of document has an obvious interest. Indeed, considering a raw vectorial description of the graphical layer of a technical document (e.g. a set of arcs and segments), signatures can be used to perform a pre-processing step before a "traditional" graphics recognition processing, or can be used to establish a classification that can be sufficient to feed a further indexation step. To compute vectorial signatures, we have based our approach on a method proposed by Etemadi et al., who study spatial relations between primitives to solve a vision problem. We considerer five types of relations, invariant to transformations like rotation or scaling, between neighboring segments: parallelism with or without overlapping, collinearity, L junctions and V junctions. A quality factor is computed for each of the relations, computable with low requirements of power. The signature of all models of symbols that could be found in a given document are computed and matched against the signature of the document, in order to determine what symbols the document is likely to contain. The quality factor associated with each relation is used to prune relations whose quality factor is too low. We present finally the first tests obtained with this method, and we discuss the improvements we plan to do

A new model for graphical object description operating in the image space or in the Cosine Discrete space

In this article a new shape descriptor – based on minimal graphs – is proposed and its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy object was studied in the context of small and low resolution binary images. The approach seems to have many interesting properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing an alternatively solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Cosine Discrete space. The use of blocks discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise with an acceptable time computing.Cet article propose un nouveau modèle de description d’un objet dans une image. Ce modèle s’appui sur la construction d’un arbre minimal, ses propriétés sont étudiées à travers le problème de la reconnaissance de symboles complexes. L’invariance de la reconnaissance – face aux translations et rotations de symboles dégradés – est vérifiée dans un contexte d’images binaires à faible résolution. Si les résultats sont concluant, le coût algorithmique peut être assez élevé. Une alternative consiste à exprimer l’objet cible dans l’espace Cosinus Discret (Transformation en Cosinus Discrète). La technique opère non plus dans l’espace image mais dans un espace compact où les données sont mieux décorrélées. Certains de nos choix font référence à des concepts de compression d’images. Cette piste conduit à une diminution sensible du coût tout en conservant un niveau de discrimination significatif. Ces résultats sont d’abord observés lors d’une expérience élémentaire puis confirmés par un test à moyenne échelle, mettant en jeu 500 symboles issus de la base de données Graphics Recognition – GREC2003