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.