Handwritten Character Recognition Using CNN Gabor-Type Filters
نویسندگان
چکیده
This paper proposes an approach for handwritten character recognition using nonlinear normalisation, a CNN Gabor-Type filter, a Location Based Dominant Orientation Map and cross correlation. Based on a test set of 26 test characters acting as template and a set consisting of 4 sets of 26 unknown handwritten test characters, max. 92 % correct recognition is provided. Recognition rate is studied for different values of the filter parameters. The results presented correspond to optimal parameter values.
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