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Author Josep Llados; Enric Marti edit  openurl
  Title A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems Type Journal Article
  Year 1999 Publication Machine Graphics & Vision Abbreviated Journal  
  Volume 8 Issue Pages 195-211  
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  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ LIM1999 Serial 1568  
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Author Josep Llados; Enric Marti edit  openurl
  Title Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction Type Journal Article
  Year 1999 Publication Machine Graphics & Vision journal, special issue on Graph transformation Abbreviated Journal  
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  Notes DAG;IAM Approved no  
  Call Number IAM @ iam @ LIM1999c Serial 1569  
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Author Josep Llados; Ernest Valveny; Enric Marti edit  isbn
openurl 
  Title Symbol Recognition in Document Image Analysis: Methods and Challenges Type Journal Article
  Year 2000 Publication Recent Research Developments in Pattern Recognition, Transworld Research Network, Abbreviated Journal  
  Volume 1 Issue Pages 151–178.  
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  ISSN ISBN 81-86846-61-1 Medium  
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  Notes DAG;IAM Approved no  
  Call Number IAM @ iam @ LVM2000 Serial 1575  
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Author Josep Llados; Enric Marti; Juan J.Villanueva edit  openurl
  Title Symbol recognition by error-tolerant subgraph matching between region adjacency graphs Type Journal Article
  Year 2001 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal  
  Volume 23 Issue 10 Pages 1137-1143  
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  Abstract The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.  
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  Notes DAG;IAM;ISE; Approved no  
  Call Number IAM @ iam @ LMV2001 Serial 1581  
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Author Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal edit  doi
openurl 
  Title A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video Type Journal Article
  Year 2011 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 44 Issue 8 Pages 1671-1683  
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  Abstract In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.  
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  Notes DAG Approved no  
  Call Number Admin @ si @ SDP2011 Serial 1727  
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