Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream |
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Authors: | Ali Borji Mandana Hamidi Fariborz Mahmoudi |
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Affiliation: | (1) School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics, Niavaran Bldg., P. O. Box 19395-5746, Tehran, Iran;(2) Computer and Information Technology Department, Azad University Branch of Zarghan, Booali Boulevard, Azad University Street, P. O. Box 73415-314, Zarghan, Iran;(3) Computer, Engineering and Information Technology Department, Azad University Branch of Qazvin, Qazvin, Iran |
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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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Keywords: | Optical character recognition Handwritten character recognition Visual system Visual ventral stream HMAX C2 features |
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