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Communication Dans Un Congrès Année : 2009

Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

Résumé

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.

Dates et versions

hal-00482922 , version 1 (12-05-2010)

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Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel. Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier. Tenth International Conference on Document Analysis and Recognition (ICDAR), Jul 2009, Barcelona, Spain. pp.1325-1329. ⟨hal-00482922⟩
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