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Author (up) Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Text/graphic separation using a sparse representation with multi-learned dictionaries Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords Graphics Recognition; Layout Analysis; Document Understandin  
  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.  
  Address Tsukuba  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ DTR2012a Serial 2135  
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