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Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits



The main aim of this work is to compare Hindu and Arabic digits with respect to a machine2019s 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), Na00EFve Bayesian and neural networks. The feature extraction methods we used were Fourier transformation and histograms.



Total Pages: 7
Pages: 295-301


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Volume: 23
Issue: 2
Year: 2016

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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