“School of Cognitive”
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| Paper IPM / Cognitive / 8936 |
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| Abstract: | |||||||
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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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