A Hybrid Approach to Detect Graphical Symbols in Documents

Tabbone, Salvatore; Wendling, Laurent; Zuwala, Daniel

doi:10.1007/978-3-540-28640-0_33

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…The work described in this paper can be pursued following several leads. First, concerning the symbol detection problem, the subgraph isomorphism approach could be applied to vector or contour-based structural descriptions of symbols and documents as the ones proposed by [37] or [41], in order to overcome some of the limits of region-based descriptions. At the same time, in order to manage possible segmentation errors which may result in merged or split vertices, operators establishing distances for one-to-many or Table 6: Precision and recall of the symbol spotting system implementing the rejection strategy based on the maximization of the F-measure many-to-one mappings [42] should be considered.…”

Section: Resultsmentioning

confidence: 99%

An integer linear program for substitution-tolerant subgraph isomorphism and its use for symbol spotting in technical drawings

Bodic

Héroux

Adam

et al. 2012

Pattern Recognition

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This paper tackles the problem of substitution-tolerant subgraph isomorphism which is a specific class of error-tolerant isomorphism. This problem aims at finding a subgraph isomorphism of a pattern graph S in a target graph G. This isomorphism only considers label substitutions and forbids vertex and edge insertion in G. This kind of subgraph isomorphism is often needed in pattern recognition problems when graphs are attributed with real values and no exact matching can be found between attributes due to noise.Our proposal to solve the problem of substitution-tolerant subgraph isomorphism relies on its formulation in the Integer Linear Program (ILP) formalism. Using a general ILP solver, the approach is able to find, if one exists, a mapping of a pattern graph into a target graph such that the topology of the searched graph is kept and the editing operations between the labels have a minimal cost.This technique is evaluated on both a set of synthetic graphs and a problem of symbol detection in technical drawings. In the second case, document and symbol images are represented by vector-attributed Region Adjacency Graphs built from a segmentation process. Obtained results demonstrate the relevance of considering subgraph isomorphism as an optimization process.

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Section: Resultsmentioning

confidence: 99%

An integer linear program for substitution-tolerant subgraph isomorphism and its use for symbol spotting in technical drawings

Bodic

Héroux

Adam

et al. 2012

Pattern Recognition

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“…In other words, a graphical element can be defined as a symbol with a particular meaning in the context of a specific application domain (Lladós et al, 2002). Our goal is very similar to spotting, but we view this as a kind of retrieval (Delalandre et al, 2010;Qureshi et al, 2008;Tabbone et al, 2004) which is basically guided by user queries.…”

Section: Motivationmentioning

confidence: 99%

BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Santosh

Wendling

2014

Int. J. Patt. Recogn. Artif. Intell.

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In 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 BoRs 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.

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“…We have worked on signatures based on force histograms [38] and on the Radon transform [36,37], which enable us to localize and recognize complex symbols in line-drawings. We are currently working on extending the Radon signature to take into account photometric information, in order to improve the results when retrieving similar symbols in graphical documents [39]. By using a higher-dimensional signature, we are able to include both the shape of the object and its photometric variations into a common formalism.…”

Section: Symbol Spottingmentioning

confidence: 99%