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

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

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

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

BoR: Bag-of-Relations for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in bags-of-relations and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications

De l'appariement de graphes symboliques à l'appariement de graphes numériques : Application à la reconnaissance de symboles

Les représentations sous forme de graphes structurels ont été appliquées dans un grand nombre de problèmes en vision par ordinateur et en reconnaissance de formes. Néanmoins, lors de l'étape d'appariement de graphes, les algorithmes classiques d'isomorphisme de graphes sont peu performants quand l'image est dégradée par du bruit ou des distorsions vectorielles. Cet article traite de la reconnaissance de symboles graphiques grâce à la formulation d'une nouvelle mesure de similarité entre leur représentation sous forme de graphes étiquetés. Dans l'approche proposée, les symboles sont d'abord décomposés en primitives structurelles et un graphe attribué est alors généré pour décrire chaque symbole. Les nœuds du graphe représentent les primitives structurelles tandis que les arcs décrivent les relations topologiques entre les primitives. L'utilisation d'attributs numériques pour caractériser les primitives et leurs relations permet d'allier précision et, invariance à la rotation et au changement d'échelle. Nous proposons également une nouvelle technique d'appariement de graphes basée sur notre fonction de similarité qui utilise les valeurs numériques des attributs pour produire un score de similarité. Cette mesure de similarité a de nombreuses propriétés intéressantes comme un fort pouvoir de discrimination, une invariance aux transformations affines et une faible sensibilité au bruit

Musings on Symbol Recognition

This paper does not pretend to be yet another survey on symbol recognition methods. It will rather try to take a step back, look at the main efforts done in that area throughout the years and propose some interesting directions to investigate

Graphics Recognition -- from Re-engineering to Retrieval

Invited talk. Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we discuss how the focus in document analysis, generally speaking, and in graphics recognition more specifically, has moved from re-engineering problems to indexing and information retrieval. After a review of ongoing work on these topics, we propose some challenges for the years to come