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Sparse representations over learned dictionary for document analysis

Abstract : In this thesis, we focus on how sparse representations can help to increase the performance of noise removal, text region extraction, pattern recognition and spotting symbols in graphical documents. To do that, first of all, we give a survey of sparse representations and its applications in image processing. Then, we present the motivation of building learning dictionary and efficient algorithms for constructing a learning dictionary. After describing the general idea of sparse representations and learned dictionary, we bring some contributions in the field of symbol recognition and document processing that achieve better performances compared to the state-of-the-art. These contributions begin by finding the answers to the following questions. The first question is how we can remove the noise of a document when we have no assumptions about the model of noise found in these images? The second question is how sparse representations over learned dictionary can separate the text/graphic parts in the graphical document? The third question is how we can apply the sparse representation for symbol recognition? We complete this thesis by proposing an approach of spotting symbols that use sparse representations for the coding of a visual vocabulary
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Submitted on : Thursday, March 29, 2018 - 12:52:23 PM
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  • HAL Id : tel-01750679, version 1



Thanh Ha Do. Sparse representations over learned dictionary for document analysis. Other [cs.OH]. Université de Lorraine, 2014. English. ⟨NNT : 2014LORR0021⟩. ⟨tel-01750679⟩



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