Comparison of normalization methods for character recognition

Image size normalization is a crucial preprocessing stage in the development of robust object recognizers. A new method of image size normalization based on multirate filter theory is proposed. Comparisons with ratio-based normalization and simple scaling are made. The effect of each normalization method on handwritten digit recognition is evaluated. Recognition incorporates global and local features extracted from normalized digit images and used with a neural network and K-nearest neighbor classifier performance evaluation is based on recognition accuracy, reject versus error graph, figure of merit and processing time required by each method. Multirate-based normalization yields better recognition performance at the cost of increased computation, whereas ratio-based normalization and simple scaling method require less processing time with reduced recognition performance