Comparison of Artificial Neural Network and Gaussian Naïve Bayes in Recognition of Hand-Writing Number

Abstract—Current technological developments spur the
application of pattern recognition in various fields, such as the
introduction of signature patterns, fingerprints, faces, and
handwriting. Human handwriting has differences between one
another and often is difficult to read or difficult to recognize
and this can hamper daily activities, such as transaction
activities that require handwriting. Even though one of the
human biometric features is handwriting. The purpose of this
paper is to compare the algorithm of Artificial Neural Network
(ANN) and Gaussian Naïve Bayes (GNB) in handwriting
number recognition. Both of these algorithms are quite reliable
in performing the classification process. ANN can do pattern
recognition and provide good results. If the size of the training
data is small, the accuracy of GNB provides good results. To
recognize the handwriting pattern, the characteristics of the
handwriting object are extracted using an invariant moment.
The test results show that GNB produces a higher level of
accuracy of 28.33% compared to the ANN of 11.67%. The
resulting accuracy level is still very low. This is because the
result extraction data has a small distance for each class or any
number character.
Keywords—handwriting, ANN, GNB, moment invariant