Retornaz, Thomas (2007) Automatic detection of text from natural scenes. A semantic descriptor for content based image retrieval. PhD thesis Morphologie Mathématique, CMM- Centre de morphologie mathématique, ENSMP p.231.

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Abstract

Multimedia data bases, both personal and professional, are continuously growing and the need for

automatic solutions becomes mandatory. Effort devoted by the research community to content-based

image indexing is also growing, but the semantic gap is difficult to cross: the low level descriptors

used for indexing are not efficient enough for an ergonomic manipulation of big and generic image

data bases. The text present in a scene is usually linked to image semantic context and constitutes a

relevant descriptor for content-based image indexing.

In this thesis we present an approach to automatic detection of text from natural scenes, which

tends to handle the text in different sizes, orientations, and backgrounds. The system uses a non

linear scale space based on the ultimate opening operator (a morphological numerical residue). In

a first step, we study the action of this operator on real images, and propose solutions to overcome

these intrinsic limitations. In a second step, the operator is used in a text detection framework which

contains additionally various tools of text categorisation.

The robustness of our approach is proven on two different dataset. First we took part to ImagEval

evaluation campaign and our approach was ranked first in the text localisation contest. Second, we

produced result (using the same framework) on the free ICDAR dataset, the results obtained are comparable

with those of the state of the art. Lastly, a demonstrator was carried out for EADS. Because

of confidentiality, this work could not be integrated into this manuscript.

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