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Author (up) Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal edit   pdf
  Title Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario Type Conference Article
  Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 31-32  
  Abstract One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DDL2017 Serial 3057  
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