This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are obtained from MNIST [1] and collected samples of digits handwritten by teachers at Da Nang University of Technology. For feature extraction, two features are chosen: Hu’s seven moments and image averaging (resizing the images to ones of less number of pixels for easier comparison). The preceding features are accompanied with corresponding classifiers, which are Neural Network classifier and Euclidean Distance. So far with the dictionary for matching collected at Da Nang University of Technology, the combination.
