Project Reading 5: Structural Offline Handwriting Character Recognition Using Levenshtein Distance

Citation

Putra, Made Edwin Wira, and Iping Supriana. "Structural Offline Handwriting Character Recognition Using Levenshtein Distance."

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Summary

This is one of the very latest research papers and discussed offline handwriting recognition using a new metric. Earlier methods of preprocessing are very expensive and use a lot of computing resources. The paper significantly improves recognition accuracy without relying on normalization techniques. The similarity metric used is Levenshtein Distance. The method was tested on digits and character images taken from ETL-1 and AIST databases. The Levenshtein distance gives accuracy of 84.69% on digits and 67.01% on alphabets. 

Discussion

In the preprocessing step the images is passed through a thresholding stage, then a thinning, and then slant correction. Features are extracted based on curve extraction, string feature representation and string graph representation. Then a string edit distance algorithm is used in this paper which is based on Levenshtein distance. The algorithm makes use of dynamic programming by using a 2-D array technique for calculating edit distance thereby speeding up the computation.  Levenshtein distance is the minimum distance required to change one string into another. The change operations are insertion, substitution and deletion.