Symbols Recognition System for Graphic Documents Combining Global Structural Approaches and Using a XML Representation of Data

Delalandre, Matthieu; Trupin, Eric; Ogier, Jean-Marc

doi:10.1007/978-3-540-27868-9_45

Cited by 1 publication

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“…Delalandre et al [7] employ a statistical technique for extracting the components (that compose the symbol) and the loops formed by these components. Afterwards, they construct graphs from these loops and deploy an inexact graph matching algorithm for recognition.…”

Section: Introductionmentioning

confidence: 99%

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

Luqman

Delalandre

Brouard

et al. 2010

Graphics Recognition. Achievements, Challenges, and Evolution

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Abstract. 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.

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Section: Introductionmentioning

confidence: 99%