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IPM
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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 8936
School of Cognitive Sciences
  Title:   Robust handwritten character recognition with features inspired by visual ventral stream
  Author(s): 
1.  Ali Borji
2.  Mandana Hamidi
3.  Fariborz Mahmoudi
  Status:   Published
  Journal: Neural Processing Letters
  No.:  2
  Vol.:  8
  Year:  2008
  Pages:   97-111
  Supported by:  IPM
  Abstract:
This paper focuses on the applicability of the features inspired by the visual ventral stream for handwritten character recognition. A set of scale and translation invariant C2 features are first extracted from all images in the dataset. Three standard classifiers kNN,ANN and SVM are then trained over a training set and then compared over a separate test set. In order to achieve higher recognition rate, a two stage classifier was designed with different preprocessing in the second stage. Experiments performed to validate the method on the well-known MNIST database, standard Farsi digits and characters, exhibit high recognition rates and compete with some of the best existing approaches. Moreover an analysis is conducted to evaluate the robustness of this approach to orientation, scale and translation distortions.

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