• Used 500 digital samples of each digit provided by university to examine feature extraction techniques in image processing and 3 different classifiers, as well as their performance and classification accuracies. • Used Matlab language and environment to analyse the performance and testing accuracy of the k-Nearest Neighbour Classifier in a handwritten digit recognition problem. • Researched and implemented diagonal-based feature extraction technique to decrease a number of original features from 256 (16x16 pixel images) to 24, and used KNN Classifier to test it • Implemented the Perceptron Algorithm to classify two handwritten digits 3 and 8 (in a digital format) • Created a Naive Bayes Classifier to classify same 2 handwritten digits