Combining neural-based approaches and linguistic knowledge for text recognition in multimedia documents
Identifieur interne : 000148 ( Main/Exploration ); précédent : 000147; suivant : 000149Combining neural-based approaches and linguistic knowledge for text recognition in multimedia documents
Auteurs : Khaoula Elagouni [France]Source :
Descripteurs français
English descriptors
- mix :
Abstract
This thesis focuses on the recognition of textual clues in images and videos. In this context, OCR (optical character recognition) systems, able to recognize caption texts as well as natural scene texts captured anywhere in the environment have been designed. Novel approaches, robust to text variability (differentfonts, colors, sizes, etc.) and acquisition conditions (complex background, non uniform lighting, low resolution, etc.) have been proposed. In particular, two kinds of methods dedicated to text recognition are provided:- A segmentation-based approach that computes nonlinear separations between characters well adapted to the localmorphology of images;- Two segmentation-free approaches that integrate a multi-scale scanning scheme. The first one relies on a graph model, while the second one uses a particular connectionist recurrent model able to handle spatial constraints between characters.In addition to the originalities of each approach, two extra contributions of this work lie in the design of a character recognition method based on a neural classification model and the incorporation of some linguistic knowledge that enables to take into account the lexical context.The proposed OCR systems were tested and evaluated on two datasets: a caption texts video dataset and a natural scene texts dataset (namely the public database ICDAR 2003). Experiments have demonstrated the efficiency of our approaches and have permitted to compare their performances to those of state-of-the-art methods, highlighting their advantages and limits.
Url:
Affiliations:
- France
- Région Bretagne
- Lorient, Rennes
- Université de Bretagne-Sud, Université de Rennes 1, Université européenne de Bretagne
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Le document en format XML
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type="abstract" xml:lang="en">This thesis focuses on the recognition of textual clues in images and videos. In this context, OCR (optical character recognition) systems, able to recognize caption texts as well as natural scene texts captured anywhere in the environment have been designed. Novel approaches, robust to text variability (differentfonts, colors, sizes, etc.) and acquisition conditions (complex background, non uniform lighting, low resolution, etc.) have been proposed. In particular, two kinds of methods dedicated to text recognition are provided:- A segmentation-based approach that computes nonlinear separations between characters well adapted to the localmorphology of images;- Two segmentation-free approaches that integrate a multi-scale scanning scheme. The first one relies on a graph model, while the second one uses a particular connectionist recurrent model able to handle spatial constraints between characters.In addition to the originalities of each approach, two extra contributions of this work lie in the design of a character recognition method based on a neural classification model and the incorporation of some linguistic knowledge that enables to take into account the lexical context.The proposed OCR systems were tested and evaluated on two datasets: a caption texts video dataset and a natural scene texts dataset (namely the public database ICDAR 2003). Experiments have demonstrated the efficiency of our approaches and have permitted to compare their performances to those of state-of-the-art methods, highlighting their advantages and limits.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Région Bretagne</li></region><settlement><li>Lorient</li><li>Rennes</li></settlement><orgName><li>Université de Bretagne-Sud</li><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><country name="France"><region name="Région Bretagne"><name sortKey="Elagouni, Khaoula" sort="Elagouni, Khaoula" uniqKey="Elagouni K" first="Khaoula" last="Elagouni">Khaoula Elagouni</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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