Contour Regularity Extraction Based on String Edit Distance
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
A new editing scheme based on a fast two-string median computation applied to OCR
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Characterization of contour regularities based on the Levenshtein edit distance
Pattern Recognition Letters
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
Pattern Recognition Letters
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An iterative learning method to update labeled string prototypes for a 1-nearest prototype (1-np) classification is introduced. Given a (typically reduced) set of initial string prototypes and a training set, it iteratively updates prototypes to better discriminate training samples. The update rule, which is based on the edit distance, adjusts a prototype by removing those local differences which are both frequent with respect to same-class closer training strings and infrequent with respect to different-class closer training strings. Closer training strings are defined by unsupervised clustering. The process continues until prototypes converge. Its main innovation is to provide a non-random local update rule to "move" a string prototype towards a number of string samples. A series of learning/classification experiments show a better 1-np performance of the updated prototypes with respect to the initial ones, that were originally selected to guarantee a good classification.