Neural Processing Letters

, Volume 28, Issue 2, pp 97–111 | Cite as

Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream



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.


Optical character recognition Handwritten character recognition Visual system Visual ventral stream HMAX C2 features 


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Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  1. 1.School of Cognitive SciencesInstitute for Studies in Theoretical Physics and MathematicsTehranIran
  2. 2.Computer and Information Technology DepartmentAzad University Branch of ZarghanZarghanIran
  3. 3.Computer, Engineering and Information Technology DepartmentAzad University Branch of QazvinQazvinIran

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