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Author (up) Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke edit   pdf
doi  openurl
  Title Keyword spotting for self-training of BLSTM NN based handwriting recognition systems Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 3 Pages 1073-1082  
  Keywords Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning  
  Abstract The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes.  
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  Area Expedition Conference  
  Notes DAG; 600.077; 602.101 Approved no  
  Call Number Admin @ si @ FFB2014 Serial 2297  
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