Assamese Character Recognition with Artificial Neural Networks

Nowadays characters that written on tablets with electronic pens are recognized and classified by computers so these are most used applications. In this study (x, y) coordinate values of hand-written Assamese characters are saved by this program. Features can be found by getting maximum, minimum, average, variant, Standard deviation and range values after size of these values are decreased by Principle Component Analysis. These features classified as Feed Forward Backpropagation Artificial Neural Network and Radial Basis Artificial Neural Network. Test results are compared after classification. In this study, online Assamese hand written tool and database of University of California Computer and Information Science department is used. Test results show that Feed Forward Backpropagation Artificial Neural Network %96 successful although Radial Basis Artificial Neural Network %82 successful.