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A String Correction Algorithm for Cursive Script Recognition

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2 Author(s)
Radmilo Bozinovic ; Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226. ; Sargur N. Srihari

This paper deals with a method of estimating a correct string X from its noisy version Y produced by a cursive script recognition system. An accurate channel model that allows for splitting, merging, and substitution of symbols is introduced. The best estimate X is obtained by using a dynamic programming search which combines a known search strategy (stack decoding) with a trie structure representation of a dictionary. The computational complexity of the algorithm is derived and compared with that of a method based on the generalized Levenshtein metric. Experimental results with the algorithm on English text based on a dictionary of the 1027 most commonly occurring words are described.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-4 ,  Issue: 6 )