Classification Experiments for Number Plate Recognition Data Set Using Weka

the Paper presents the comparison of different classification techniques for the task of classifying Number plate image data set. The comparison was conducted using WEKA (Waikato Environment for Knowledge Analysis) open source which mainly consists the collection of machine learning algorithms for data mining purpose. The main purpose of this paper is to investigate efficiency of different classification methods by applying on the Number Plate Image data set. Which will further used in Number Plate Recognition process. The methods or algorithms performed are J48 (Decision Tree), MLP (Multilayer Perceptron), Naïve Bayes, K-NN (K-Nearest Neighbors) and the result of MLP was better than others received in range of 78% 96% as compared to other techniques. Keywords— J48(Decision Tree), MLP, Naïve Bayes, K-NN.