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Author (up) Nuria Cirera; Alicia Fornes; Josep Llados edit   pdf
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  Title Hidden Markov model topology optimization for handwriting recognition Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 626-630  
  Keywords  
  Abstract In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task.		  
  Address Nancy; France; August 2015  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.061; 602.006; 600.077 Approved no  
  Call Number Admin @ si @ CFL2015 Serial 2639  
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