Comparing the machine ability to recognize handwritten Hindu and Arabic digits
, Khalil El Hindi, Muna Khayyat, Areej Abu Kar . 2016
The main aim of this work is to compare Hindu and Arabic digits with respect to a machine’s ability
to recognize them. This comparison is done on the raw representation (images) of the digits and on
their features extracted using two feature selection methods. Three learning algorithms with different
inductive biases were used in the comparison performed using the raw representation; two of them
were also used to compare the digits using their extracted features. All classifiers gave better results
for Hindu digits in both cases; when raw representation was used and when the selected features
where used. The experiments also show that Hindu digits can be classified with better accuracy, higher
confidence and using fewer features than Arabic digits. These results indicate that hand-written Hindu
digits are actually easier to recognize than hand-written Arabic digits. The machine learning methods
used in this work are instance based learning (the kNN algorithm), Naïve Bayesian and neural networks.
The feature extraction methods we used were Fourier transformation and histograms.
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