Character recognition of optically blurred textual images using moment invariants
Statistical moment invariants were used to generate a feature space for classifying images of text characters. The feature vector of a given letter is invariant to changes in scale, position, rotation, and contrast in the image. Test character images were generated by simulated optical blurring. Images were classified by calculating the distance between the feature vector of a given test character and that of each reference character. The test character was identified as the reference character for which the distance between feature vectors is a minimum. Significantly blurred characters were classified correctly using this method.