Text/graphic separation using a sparse representation with multi-learned dictionaries
Identifieur interne : 001881 ( Main/Merge ); précédent : 001880; suivant : 001882Text/graphic separation using a sparse representation with multi-learned dictionaries
Auteurs : Thanh Ha Do [France] ; Salvatore Tabbone [France] ; Oriol Ramos Terrades [Espagne]Source :
English descriptors
Abstract
In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.
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In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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