Traditionally, zero crossings of the second derivative provide edge features for the classification of blurred waveforms. The accuracy of these edge features deteriorates in the case of severely blurred images. In this paper, a new feature is presented that is more resistant to the blurring process, the image, and waveform peaks. In addition, an estimate of the standard deviation /spl sigma/ of the blurring kernel is used to perform minor deblurring of the waveform. Statistical pattern recognition is used to classify the peaks as bar code characters. The noise tolerance of this recognition algorithm is increased by using an adaptive, histogram-based technique to remove the noise. In a bar code environment that requires a misclassification rate of less than one in a million, the recognition algorithm showed a 43% performance improvement over current commercial bar code reading equipment.>

An algorithm for processing closely spaced edges and accurately restoring their locations is presented. The convolution distortion model is based on interacting edges. The restoration algorithm takes three edges as input and forces the effects of two of them to cancel each other. Thus the third edge appears to be isolated and is located using a traditional edge detector. Experiments on bar code waveforms reveal that this approach outperforms the current commercial bar code readers.