Bar code waveform recognition using peak locations
Joseph, E.
Pavlidis, T.
R&D Dept., Symbol Technol. Inc., Bohemia, NY ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1994
Volume: 16,
Issue: 6
On page(s): 630-640
ISSN: 0162-8828
References Cited: 22
CODEN: ITPIDJ
INSPEC Accession Number: 4722519
Digital Object Identifier: 10.1109/34.295907
Current Version Published: 2002-08-06
Abstract
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 σ 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
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