Comparison of Statistical and Structural Features for Handwritten Numeral Recognition

This paper compares the recognition accuracy of handwritten numerals achieved using statistical and structural features. Both features are trained and tested using neural network. In order to get good features, digit images are undergone various preprocessing activities. The operations such as noise removal, thresholding, linking broken digit, rotation, pruning and cropping are done before feature extraction. The recognition rate obtained using statistical and structural features are 93.3% and 95.7% respectively.