“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 11158 |
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Humans are very efficient in recognizing alpha numeric characters,even in the presence of significant image distortions. Recent advances in visual neuroscience have led to a solid model of object and shape recognition in the visual ventral stream which competes with the stateof-the-art computer vision systems on some standard recognition tasks. A modification of this model is also proposed by adding more biologically inspired properties such as sparsification of features, lateral inhibition and feature localization to enhance its performance. In this study, we show that using features proposed by the modified model
results in higher handwritten digit recognition rates compared with the original model over English and Farsi handwritten digit datasets. Our analyses also demonstrate higher invariance of the modified model to various image distortions.
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