Fusion of statistical and structural information for flowchart recognition

Abstract : A critical step of on-line handwritten diagram recognition is the segmentation between text and symbols. It is still an open problem in several approaches of the literature. However, for a human operator, text/symbol segmentation is an easy task and does not even need understanding diagram semantics. It is done thanks to the use of both structural knowledge and statistical analysis. A human operator knows what is a symbol and how to distinguish a good symbol from a bad one in a list of candidates. We propose to reproduce this perceptive mechanism by introducing some statistical information inside of a grammatical method for document structure recognition, in order to combine both structural an statistical knowledge. This approach is applied to flowchart recognition on a freely available database. The results demonstrate the interest of combining statistical and structural information for perceptive vision in diagram recognition.
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Communication dans un congrès
ICDAR - International Conference on Document Analysis and Recognition, 2013, Washington, United States. pp.1242-1246, 2013
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  • HAL Id : hal-00921640, version 1

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Cérès Carton, Aurélie Lemaitre, Bertrand Couasnon. Fusion of statistical and structural information for flowchart recognition. ICDAR - International Conference on Document Analysis and Recognition, 2013, Washington, United States. pp.1242-1246, 2013. 〈hal-00921640〉

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