Offline Cursive Character Recognition : A state-ofthe-art comparison

  title={Offline Cursive Character Recognition : A state-ofthe-art comparison},
  author={John Thornton and Jolon Faichney and Michael Blumenstein and Vu Nguyen and Trevor Hine},
Recent research has demonstrated the superiority of SVM-based approaches for offline cursive character recognition. In particular, Camastra’s 2007 study showed SVM to be better than alternative LVQ and MLP approaches on the large C-Cube data set. Subsequent work has applied hierarchical vector quantization (HVQ) with temporal pooling to the same data set, improving on LVQ and MLP but still not reaching SVM recognition rates. In the current paper, we revisit Camastra’s SVM study in order to… CONTINUE READING
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